Pespectives on Clinical Decisions

Symposium on the Role of the Laboratory in Clinical Decision Making -Part H Robert S. Galen, M.D., M.P.H., and M. Desmond Burhe, M.D., Guest Editors

Perspectives on Clinical Decisions Ben T. Williams, M.D.*

Abstract The critical role of expert j u d g m e n t in clinical decision support has been accommodated in a class of approaches termed knowledge based systems. These have arisen from work in artificial intelligence on expert performance. Some of the perspectives and insights that have been gained from these approaches are briefly discussed.

Other articles in this issue on the Role of the Laboratory in Clinical Decision Making describe approaches that have been characterized by Szolovits and Pauker ~ as categorical and probabilistic approaches that have been characterized by Szolovits may also be generally regarded as deterministic, qualitative, binary, and the like and are reflected in decision tables, flow charts, decision trees, and similar instruments in which each step of the decision making process depends on clear-cut criteria, which are or are not satisfied (though the criteria themselves may be based o n a continuous scale). Probabilistic approaches include those that may be characterized as 9 statistical or quantitative or that involve continuous variables. These techniques are reflected in such procedures as discriminant analysis, Bayesian formulations, predictive value theory, and decision analysis. Clearly there is considerable overlap, as when categorical approaches are used to develop a decision tree with probabilistic approaches used to perform a decision analysis on that tree and select a preferred path, which then becomes a flow chart for deployment to users as a categorical instrument. There are still other viewpoints from which decision support systems have been explored, notably those of pattern recognition and numerical processes, but these may be regarded as special cases or different perspectives

of categorical and probabilistic approaches. 2"a There have been recent reviews of such decision support systems.4.5 As is exemplified in other articles in this symposium, these now more "traditional" approaches have proven to be powerful for the support of clinical decisions, particularly in decisions that are dominated by a re.latively small number of relatively independent, exhaustive, and exclusive variables, in which data are a~;ailable or can readily be gathered for probabilistic formulation and in which patient data are available at the time needed for sequential decisions. These are decision problems in which local and seasonal variations in prevalence can be managed (usually manually), disease and management taxonomies and definitions are stable, all the relevant and dominant variables with their associated'assumptions and exceptions can be readily articulated by the professional, there is general and individual agreement on outcome values or utilities, and means for deployment of the decision support instruments are at hand. These characteristics are found in many common but difficult decision problems in medicine in which formal decision support may be desirable. On the other hand, decision support may be most needed at the expert consulting level when nonstandard and poorly understood or poorly structured problems are

Accepted fin publication February "15, 1980. *Clinical Professor of l'atllology and Medical Information Science, University of Illinois College of Medicine, School of Clinical Medicine at Urbana-Champaign. Director of Laboratories, Mercy |{ospital, Urbana, Illinois. HOMAN I'ATHOLOGYIVOLUME

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PERSPECTIVES ON CLINICAL DECISIONS--WILLXA.Xls presented. In these situations many of the preceding desiderata are either lacking or unverifiable. Further, as problem complexity increases, a greater number of variables is required to characterize the reality from which the problem statement is abstracted. Increasing the number of variables leads to major problems that may include theoretical limits in addition to practical difficulties. In the first place the cohort subpopulation may become vanishingly small and eventually unique ("N= 1"). Second, the mathematical aspects of treating interactions between variables may become too large for feasible solution, since the mathematical burden increases exponentially as the number of variables increases arithmetically. This is the phenomenon described as combinatorial explosion. These and related difficulties in the use of purely categorical and purely pr0babilistic approaches have been described by Edwards 6 and Feinstein. 7-1~ Yet human experts appear to make useful contributions to solving such problems; further, there is clearly a difference in the facility and success with which human experts manage such problems. Finally, as Bosk u has noted, clinical expertise dominates scientific evidence in reaching clinical decisions. This is because the expert considers the assumptions and exceptions t o the rules, as well as the rules themselves: "the logic of one's own experience determiiaes the usefulness of the research o f others." For these reasons decision theorists in clinical medicine have been drawn in recent years to the insights gained in the study of human expert performance by the artificial intelligence community. As Simon ~2 has noted, artificial intelligence efforts in decision theory have been concerned with how to render such "messy" and complex problems more tractable. As a consequence, such artificial intelligence systems become quite complex, leading to difficulty in concise description, not to mention the problems that may be encountered in machine performance. Often one may get an impression about the operation of such systems only through operational use. Nevertheless, this article provides a brief review of some of the perspectives brought to clinical decision making by the discipline of artificial intelligence, and a simplified overview is presented of the specific artificial intelligence approaches that are embodied in so-called "knowledge based systems," the design and development of which have been termed by Michie "knowledge engineering. '''3 ARTIFICIAL INTELLIGENCE APPROACHES

As noted by Simon, 12 decision making in the real world involves not only a choice among alternative courses of action (the dominant application o f traditional approaches), but also the discovery of those alternatives and the scanning of the environment for opportunities in which decision making is appropriate. To do this, artificial,intelligence workers use a "bag of tricks," much of which was developed in

earlier artificial intelligence tasks such as those of image analysis, game theory, linguistics and languag.e translation, automatic programming, automatic theorem proving, and the like. The bag o f tricks of artificial intelligence includes notions such as those of heuristic search, of the fractionation or modularization of problems into subproblems with goals and subgoals, of plausible reasoning, and o f melioration. Heuristic search is concerned with the development of efficient means for searching a structured space of hypotheses--diseases, management plans, and the like - - to discard at each step those that are irrelevant and retain those that are of significance to problem solving in specific cases. A n u m b e r of search techniques ha~.,e been developed that are appropriate to different types of problems or even to different stages of solution of a problem. In "knowledge based systems" as applied to medicine, heuristic search is used to scan the descriptive knowledge base and to select those hypotheses relevant to clinical decision making in specific cases. The fractionation of a complex problem into subproblems is a strategy developed to avoid the combinatorial complexity that occurs if one attempts to treat all variables relevant to complex problem solving at the same time. In order to accomplish this, a goal structure is developed through which higher level goals are achieved through the accomplishment of lower level goals. Integration of the solutions to lower level problems facilitates tile achievement of the higher level goals. Plausible reasoning refers both to the strategic options--searching, focusing, discriminating, confirming, and the like - - available at different stages of the problem solving process and to the tactical rules to be used, u n d e r strategic control, to solve specific problems in detail. Tactical reasoning is usually concerned with the selection of alternatives and employs both classic categorical and probabilistic reasoning, but also employs a range of techniques that have been characterized as subjective probabilky or plausible inference. These procedures for the management of uncertainty are particularly suited to the incorporation of expert j u d g m e n t when statistical data are lacking, when violations of statistical assumptions render traditional statistical approaches improper, and when the number of' variables involved renders classic probabilistic approaches infeasible owing to combinatorial complexity. Such plausible reasoning involves specific techniques for the representation of uncertainty, and since uncertainty measures must often be propagated from one level of problem solving to another owing to modularization, expert j u d g m e n t is reflected in the propagation of inference within and between levels of abstraction. Nontraditional probabilistic techniques in the sense used here include those o f confidence theory, confirmation theory, belief theory, fuzzy logic, varivalued logic, and the likeY 14-16 Melioration incorporates the notion that "the best is the enemy of the good." In many complex

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HUMAN PATHOLOGY--VOLUME 12, NUMBER 2 problems the optimization sought in traditional approaches is not feasible owing to the limitations imposed by combinatorial complexity. This is apart from problems imposed by differing values as to what is optimal. The goal of the problem solving system should be to develop solutions that are "pretty good," "good enough," or at least as good as those commonly made by experts in the field. Hence, the standards of performance .are defined differently and other perspectives of success are developed. At the same time, since the characterization of performance is not absolute, the evahmtion of specific systems often becomes complex. In addition to these features that are the common heritage of artificial intelligence programs in many areas, those programs that have been concerned with clinical decision support have involved a subset of artificial intelligence programs characterized as "knowledge based systems," "pattern directed inference systems, ''lz and "expert systems." The additional features os such knowledge based systems are now described. '

KNOWLEDGE BASED SYSTEMS T h e key feature of knowledge based systems is the deliberate separation of the knowledge base of a domain, such as a medical specialty, from the "inference engine" used in reasoning. The inference engine might involve a classic Bayesian formulation 6r, in most expert knowledge based systems, involves some form of subjective probabilistic reasoning as previously noted. In using the classic Bayesian example, the inference engine would contain a generalized Bayesian formula for which it is necessary to designate the specific variables to be used and then to give quantitative expression to those variables, i.e., the a priori and a posteriori figures that are appropriate to the problem. These variables and these quantitative figures are contained in the knowledge base. As patient data arrive to identify and characterize a specific problem, the patient data act as a categorical probe of the knowledge base, often by a process of partial pattern matching, to find and fetch the appropriate knowledge, carried as facts and hypotheses incorporating the variables and their quantitative features, which are then plugged into the inference engine. Thus there is a deliberate separation of the knowledge base from the inference engine. The emphasis on knowledge of the domain has some counterpart in human studies indicating that the feature that most differentiates the expert from the routine practitioner is not a difference in problem solving skills but in the range and detail of the knowledge of the expert. TM The knowledge base itself may be subdivided into a descriptive or factual knowledge base and a normative or procedural knowledge base. The normative knowledge base contains the inference rules that compose the tacticalknowledge of the system used in detailed problem solving and the

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February 1981 strategic knowledge base that provides overall system control and indicates at which point specific tactical knowledge shotfld be applied. The strategies themselves may be layered; i.e., there, may be higher level strategies to control lower level strategies. For example, the initial stages of decision support may involve a subgoal of problem identification and the development of a model of the problem. T h e strategy of problem identification may then call up subsidiary strategies of data acquisition, of searching the space of hypotheses, and of focusing on a subset o f these. The overall strategy then moves to one of problem formulation in which subsidiary strategies involved with the development of goal structures and subproblems may be invoked. Control then passes to specific problem solving strategies involving selection among alternatives, in which subsidiary strategies of confirmation, discrimination, exclusion, grouping and confirmation or exclusion, or further exploration may be called for. Should problem solving at this stage be successful (for example, in uniquely identifying a most probable diagnosis with a high degree of confidence), strategies then may move to elaboration of staging, associated functional disability, prognosis, and acquisition of the additional data that may be needed for management decisions. The overall strategy of development of a m a n a g e ment plan may proceed using similar subsidiary strategies of searching, focusing, and choice. In addition to the use of knowledge based systems for reasoning, such systems also require a knowledge acquisition component and an explanatory component. The knowledge acquisition component is concerned with facilitating the acquisition of knowledge from experts, and perhaps with its later modification a n d tint tuning on the basis of operational experience. Not only must the knowledge acquisition component derive and structure knowledge from experts in a form that is required for use by the inference engine, it must also help the expert to characterize the context in which each inference is apwopriate and must help to tease out any hidden assumptions and exceptions that may be relevant to the use of the inference. The explanation and query component is important to the acceptance and use of the knowledge based system for real problems. The query component allows the user to extract knowledge as it is contained in the system according to its content or meaning. T h e explanatory component is related to the query component but is concerned with providing traces of reasoning in specific cases. T h e explanatory component may thus be said to provide a "white box" or "transparent box" for the user, who is uhimately responsible for the decisions supported by the system. This is necessary because of the different types of reasoning that are employed at different stages of the problem solving process and that occur automatically and without deliberate-user direction. If the user disagrees with the course of reasoning of the program, this disagreement may help to uncover as-

PERSPECTIVES ON CLINICAL DECISIONS--~NILLIAMS sumptions about the use of specific inferences that were heretofore hidden and then to promote their incorporation into the program. KNOWLEDGE REPRESENTATION

The key issue in the development of a descriptive knowledge base is the issue of how that knowledge is to be represented. As implied, the descriptive or factual knowledge base is composed of facts. A fact is defined for purposes of such systems as the relationship between two things. Thus knowledge representations must be developed that allow translation of a subject matter model (itself derived from reality), such as that contained in a textbook, into a more formal method of knowledge representation that may be managed within the machine. The form of knowledge representation chosen not only must satisfy the demands of the reasoning portions of the system but must also support the knowledge acquisition and explanatory functi6n$. Knowledge representation schemes in clinical expert systems are often divided into semantic networks, frame systems, and production rule systems. There are several variations of each. Another taxonomy that may be used is that of network based systems and rule based systems. T h e control structure of the former is more complex, with separate mechanisms for strategic guidance. Production rule systems use a more uniform control strategy throughout, but the housekeeping problems of keeping track o f system function may b e more severe as the system increases in scope. With the recent development of hybrid systems the distinctions between network based and rule based systems are becoming obscure and the advantages of each approach contribute to improved performance? 7 Semantic networks were primarily derived in the context of linguistic work. Semantic nets are composed of nodes and links or arcs. The nodes represent t h i n g s - - r e a l objects, abstract objects, concepts, specific people, and the like. The arcs or links represent the relations between {hings or nodes. These relations have both a qualitative and a quantitative dimension. Qualitative dimensions include causal links, associational links, taxonomic links, and pattern or grouping links. T h e quantitative dimension delineates the strength of the link, and this may differ in different contexts. The strength of the link is a measure of uncertainty and probability. Things may be regarded as findings and hypotheses. There are rules and relations necessary to link findings to hypotheses, findings to other findings, and hypotheses to other hypotheses, perhaps at different levels of abstraction. A hypothesis may represent a disease, a pathophysiologic state, a clinical state, a prognosis, a stage, or a management plan. As patient findings become available to probe the factual knowledge base, a largely categorical function, candidate hypotheses are identified, additional findings

may be requested, and some preliminary focusing may occur. Later, in the more detailed selective aspects of the problem solving process, it may be necessary to discriminate among alternative hypotheses, to rule out some, or to confirm one or more. This is done through the probabilistic or subjective probabilistic mechanisms embodied in the inference engine. Those variables appropriate to the hypothesis being tested are embedded in the knowledge base as are the relationships necessary for the operation of the inference engine. Here the final stages of the problem solving process occur in a manner similar to that found in more traditional forms of probabilistic reasoning. Since categorical mechanisms are largely used for initial search and focus, and probabilistic mechanisms are employed for choice, it has been noted that "categorical proposes, probabilistic disposes. ''t Production rule systems operate with a simpler control strategy, often with a contextual description of the patient or other aspects of the problem carried separately. Production systems are derived from process control origins and are developed through the chaining of production rules. A production rule is composed of a situation-action pair. If the situation, which may be composed of more than one variable, is present, the rule fires and the action is carried out or the conclusion is reached. Such rules also include a quantitative aspect. Production rule systems have a tree shape similar to that of a flow chart rather than a network structure. Indeed, semantic nets have been likened to production rule trees with cross indexing between the branches. Production systems may operate in a forward or reverse chaining mode. In medicine such systems have been used primarily for management decision support, and the most highly developed programs operate in a reverse chaining mode. In this mode, a con~zlusion or intermediate conclusion is reached if the situation portion of the rule is satisfied. However, the situation portion of the rule becomes the conclusion o f an antecedent rule, and through recursive r e v e r s e chaining one may identify whether the data required for the activation of the whole pathway are present and positive. If these data have not already been entered into the system or cannot be inferred from other data, the user is asked. OPERATIONAL SYSTEMS During the past few years several knowledge based systems have achieved some degree of operational stature for clinical decision support. Perhaps more importantly, these systems have provided the initial prototype experience for subsequent refinement in the development of second generation systems. The Present Illness Pl;ogram (P.I.P.) is a frame based system for acquisition of the present illness and for its integration with other findings in the diagnosis

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of renal disease? 9,2~ It was developed through the collaboration of the Massachusetts Institute of Technology and Tufts University, and current work is progressing on second generation systems in the area of electrolyte and acid-base balance. The INTERNISTprograms have been developed at the university of Pittsburg to support differential diagnosis in internal medicineY ~'2" This network based system has been tested, with impressive success, on the Clinical Pathological Conferences of the Massachusetts General Hospital as published in the New En.glandJournal of Medicine. This system is now undergoing extensive revision. The CASNET (Causal-Associational Network) system developed at Rutgers University is a semantic network system designed for expert consuhation in the diagnosis and management of ocular disorders3 a,24 It has been tested at major ophthalmological conferences. Current work is on the EXPERT system, a rule based second generation system now under development in the areas of rheumatology, neurology, and i~0docrinology3 ~ The MYCIN system, a production rule system developed and tested at Stanford University for consultation in the management of infectious disease, has now been extended into other domains, including respiratory disease and oncology, ta'z~ All these systems use laboratory data along with clinical data in clinical decision support, and in some, laboratory data are dominant. Other centers now have muhifaceted programs underway for the develo p m e n t of these decision support systems, including Harvard University and the University of IllinoisY "z9 More extensive discussion may be found in the references cited and in a forthcoming book. a~

SUMMARY Much of this issue is concerned with extending the range and usefiflness of the application of traditional categorical and probabilistic approaches to clinical decision suppor.t. These approaches are of growing importance when a train of decisions is dominated b y one or two major decision steps, each step of which is dominated by relatively small numbers of variables. However, as problem complexity increases, as is seen with increasing frequency in complex management decisions, the attempt to reify expert j u d g m e n t in machine programs becomes more and more attractive. Artificial intelligence has provided the primary tools to attempt this machine abstraction of expert behavior. The approaches and techniques that have been tised in artificial intelligence for clinical decision support, i.e., knowledge based system approaches, have been briefly described. Since professionals engaged in human pathology not only generate much of the information to be used in expert consulting systems but have often provided the focus for the deployment and management of current decision

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support systems, the increasing involvement of pathologists in the development and evolution of advanced systems appears desirable. REFERENCES 1. Szolovits, P.., and Pauker, S. G.: Categorical and probabilistic reasoning in medical diagnosis. J. Artif. Intell., 11:115, 1978. 2. Patrick, E. A., Stelmack, F. P., and Shen, L. Y. L.: Review of pattern recognition in medical diagnosis and consulting relative to a new system model. IEEE Trans. Syst. Man. Cyber., SMC-4(I), 1974, p. 1. 3. Bleich, H. L.: Computer evaluation of acid-base disorders. J. Clin. Invest., 48:466, 1975. 4. Rogers, W., Ryack, B., and Moeller, G.: Computer-aided medical diagnosis: literature review. Int. J. Bio-Med. Comp., 10:267, 1979. 5. Patrick, E. A.: Decision analysis in medicine: methods and applications. Boca Raton, Florida, CRC Press, 1979. 6. Edwards, W.: N = 1: diagnosis in unique cases. In Jacquez, j. D. (Editor): Computer Diagnosis and Diagnostic Methods. Springfield, Illinois, Charles C Thomas, 1972, p.139. 7. Feinstein, A. R.: Clinical bio-statistics. XX1X. The haze of Bayes. The aerial palaces of decision analysis and the computerized ouija board. Clin. Pharm. Therap., 21:483, 1977. 8. Feinstein, A. R.: An analysis of diagnostic reasoning. I. The domains and disorders of clinical macrobiology. YaleJ. Biol. Med., 46:212, 1973. 9. Feinstein, A. R.: An analysis of diagnostic reasoning. II. The strategy of intermediate decision. Yale J. Biol. Med., 46:264, 1973. 10. Feinstein, A. R.: An analysis of diagnostic reasoning. III. The construction of clinical algorithms. Yale J. Biol. Med., 1:5, 1974. 11. Bosk, C. L.: Forgive and Remember. Chicago, University of Chicago Press, 1979. 12. Simon, H.: Artificial intelligence and decision making. Visiting lecture, Coordinated.Science Laboratory, University of Illino.is, Urb,'ina, 14 November, 1979. 13. Good, I. J.: Dynam!c probability, computer chess, and the measurement of knowledge. In Elcock, E. W., and Michie, D. (Editors): Machine Representations of Knowledge: Machine Intelligence. New York, John Wiley & Sons, Inc., 1977, p.139. 14. Shortliffe, E. H.: Computer-based Medical Consultations: .~t~'clx. New York, Elsevier-North Holland Pub CO., 1976. 15. Shafter, G.: A Mathematical Theory of Evidence. Princeton, Princeton University Press, 1976. 16. Michalski, R. S.: Discovering classification rules nsing variablevalued logic system. VL 1, Proc. 3 Int. Joint Conf. Artif. Intell., Stanford University, Stanford, 1973. 17. Waterman, D. A., and Hayes-Roth, F. (Edito]-s): Patterndirected hfference Systems. New York, Academic Press, Inc., 1978. 18. Elstein, A. S., SImhnan, L. S., and Sprafka, S. A.: Medical Problem Solving: An Analysis of Clinical Reasoning. Cambridge, Harvard University Press, 1978. 19. Pauker, S. G., et al.: To~(ards the simulation of clinical cognition: taking a present illness by computer. Am.J. Med., 60:981, 1976. 20. Szolovits, P., and Pauker, S. G.: Research on a medical consultation system for taking the present illness. In Proc. 3 I11. Conf. Med. Info. Sys., Chicago, November 1976, p. 299. 21. Pople, H. E., Jr.: The formation of composite hypotheses in diagnostic problem solving: an exercise in synthetic reasoning. Proc. 5 Int. Joint Conf. Artif. Intell. Dept. Comp. Sci., Carnegie-Mellon Univ., Pittsburgh, Pa., 1977. 22. Myers, J. D., and Pople, H. E.: A eonstfltative diagnostic program in internal nl'edicine. Proc. 1 Ann. Symp. Comp. Comp. Applic. in Med. Care, Wastfington, D.C., 1977, p. 52.

COST EFFECTIVENESS OF MULTIPHASIC SCREENING--WEa.~ER, ALTSttULER 23. Kulikowski,C. A.: Artificial intelligence approaches to medical consultation. Proc. 4 III. Conf. Med. Info. Syst., May 1978, p. 162. 24. Kulikowski, C.A.: The design of expert-level consultation systems: CASNETand EXPERTformalisms. Proc. Symp. Artif. Intell. Med., Tokyo, Japan, November 1978. 25. Politakis, P., Weiss, S., and Kulikowski, C.: Designing consistent knowledge bases for expert consultation systems. Proc. 13 Ann. Hawaii Int. Conf. Syst. Sci., Honolulu, Hawaii, January 1980, p. 675. 26. Yu, v. L., et al.: Antimicrobial selection by a computer. J.A.M.A.,242:I279, 1979.

27. Greenes, R. A.: The diagnostic test order decision problems. In Proc. 6 Ill. Conf. Med. Info. Sys., Urbana, April 1980. (To be published.) 28. Levy, A. H., and Baskin, A. B.: A paradigm for medical problem solving. Proc. 12 Hawaii Int. Conf. Sys. Sci., Honolulu, Hawaii, 1980. 29. Baskin, A. B., and Levy, A. H.: .XfEOmas--an interactive knowledge acquisition system. Proc. 2 Ann. Symp. Comp. Applic. Med. Care, November 1978, p. 344. 30. Wilhams, B. T., Galen, R., Pass, T., and Fink, D.i Computer Aids to Clinical Decisions. Boca Raton, Florida, CRC Press, 1980. Mercy Hospital 1400 West Park Avenu~ Urbana, Illinois 61801

C O S T E F F E C T I V E N E S S OF M U L T I P H A S I C SCREENING: OLD CONTROVERSIES AND A NEW RATIONALE Mario Werner, M.D.,* and Charles H. Altshuler, M.D.'~

ABSTRACT T h e cost effectiveness o f multiphasic screening is evaluated f r o m a conceptual as well as f r o m a practical viewpoint. C o n c e p t u a l analysis includes a consideration o f the technical sensitivity and specificity o f the tests used, o f the prevalences o f the.screened diseases, a n d o f the costs and values associated with d i f f e r e n t outcomes o f screening. Practical considerations include the potential o f muhiphasic screening for increasing productivity, for reassuring patients, a n d for r e d u c i n g morbidity and mortality. A l t h o u g h all these issues can be cogently f o r m u l a t e d , at present n o n e can be d o c u m e n t e d by a c o m p r e h e n s i v e set o f data leading to irrefutable conclusions. T h e r e f o r e , the issue o f who should be screened continues to be obfuscated by controversy and prejudice. T o resolve this dilemma a new rationale f o r the use o f multiphasic screening is developed. This is based on a small n u m b e r o f uncontroversial facts a n d leads to practical proposals relating to how standards for useful test batteries can be constructed.

Insight into the cost effective use o f laboratory tests has greatly increased both from a conceptual a n d f r o m a practical viewpoint, but these two views seem as far as ever f r o m m e r g i n g into some u n i f y i n g doctrine. 1"3 T h e need for s u c h - a n integration has

been most noticeable in the use o f s t a n d a r d test profiles a n d multiphasic screening, since strongly held tenets a r g u e both for a n d against that a p p r o a c h to diagnosis. 4 This discussion o f the cost effectiveness o f

Accepted for publication February 15, 1980. *l'rofessor of l'athology (Laboratory Medicine), Tile George Wasbington University Medical Center, Washington, D.C. "{Clinical Professor of Pathology, Medical College of Wisconsin. Director, Del)artment of l'atho/ogy, St. Jnseph's Hospital, Milwaukee, Wisconsin. HUglAN PATHOLOGY--VOLUME 12, NUMBER 2 February 1981

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