Interpretation by Physicians of Clinical Laboratory Results

Casscells, Ward; Schoenberger, Arno; Graboys, Thomas B.

doi:10.1056/nejm197811022991808

Cited by 665 publications

(296 citation statements)

References 16 publications

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“…We know from several studies that physicians, college students (Eddy, 1982), and staff at Harvard Medical School (Casscells, Schoenberger & Grayboys, 1978) all have equally great difficulties with this and similar medical disease problems. For instance, Eddy (1982) reported that 95 out of 100 physicians estimated the posterior probability p(cancer | positive) to be between 70% and 80%, rather than 7.8%.…”

Section: Standard Probability Formatmentioning

confidence: 99%

How to improve Bayesian reasoning without instruction: Frequency formats.

Gigerenzer¹,

Hoffrage²

1995

Psychological Review

1,758

1,789

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Is the mind, by design, predisposed against performing Bayesian inference? Previous research on base rate neglect suggests that the mind lacks the appropriate cognitive algorithms. However, any claim against the existence of an algorithm, Bayesian or otherwise, is impossible to evaluate unless one specifies the information format in which it is designed to operate. The authors show that Bayesian algorithms are computationally simpler in frequency formats than in the probability formats used in previous research. Frequency formats correspond to the sequential way information is acquired in natural sampling, from animal foraging to neural networks. By analyzing several thousand solutions to Bayesian problems, the authors found that when information was presented in frequency formats, statistically naive participants derived up to 50% of all inferences by Bayesian algorithms. Non-Bayesian algorithms included simple versions of Fisherian and Neyman-Pearsonian inference.

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Section: Standard Probability Formatmentioning

confidence: 99%

How to improve Bayesian reasoning without instruction: Frequency formats.

Gigerenzer¹,

Hoffrage²

1995

Psychological Review

1,758

1,789

View full text Add to dashboard Cite

show abstract

“…As such, the range-frequency model might not be directly applicable. However, some features of the model, such as the tendency to use categories with equal frequencies, might explain the general tendency to ignore base rates (Balla, Elstein, & Gates, 1983;Casscells, Schoenberger, & Grayboys, 1978;Kahneman & Tversky, 1973;Meehl & Rosen, 1955). Furthermore, insofar as these three more specific axes tend to tap more global evaluative dimensions, similar contextual effects should be expected, and range-frequency theory would be directly applicable.…”

Section: Discussionmentioning

confidence: 99%

Reducing the dependence of clinical judgment on the immediate context: Effects of number of categories and type of anchors.

Wedell¹,

Parducci²,

Lane³

1990

Journal of Personality and Social Psychology

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Two experiments explored methods for standardizing ratings of the psychopathology of clinical case histories. In both experiments, the same case histories were rated as more pathological when mostly mild rather than severe cases were presented as the immediate context. Psychometric analyses demonstrated that this type of contextual effect is a potentially important source of unreliability in clinical judgment. In Experiment 1, increasing the number of points in the rating scale from 3 to either 7 or 100 significantly reduced the effects of the immediate context. Ratings were parsimoniously modeled by Parducci's (1983) range-frequency theory. In Experiment 2, providing verbal anchors in the form of either detailed DSM descriptions for each rating category or sample case histories for the two end-categories increased the reliability of the ratings by reducing the effects of the immediate contexts; however, these reductions occurred only when the ranges of the immediate contexts had been severely restricted. According to the range-frequency analysis, verbal anchors served to equate the endpoints of the subjective range for the different contextual conditions. Comparison with previous research suggests that the anchors also reduced the effects of the sequential position in which clinical cases appear. We therefore recommend that studies of the reliability of behavioral assessment techniques take into account the effects of differences in context.Although reliability does not imply validity, it is a necessary prerequisite. Unfortunately, clinical assessment in psychology is notoriously unreliable. Eysenck, Wakefield, and Friedman (1983) have documented the lack of interrater reliability. Goldberg (1968Goldberg ( , 1970 concluded that clinical judgments tend to be unreliable and only minimally related to the degree of experience of the person making the assessment. Arnoff (1954) reported an actual decrease in reliability with greater clinical experience.One generally accepted method for improving clinical prediction has been the use of statistical models. In particular, there is overwhelming evidence for the superiority of simple linear models over clinicians in combining information to form a diagnosis (Dawes, 1979;Dawes&Corrigan, 1974;Einhorn, 1972;Goldberg, 1968Goldberg, , 1970Meehl, 1954). But as Sawyer (1966) has pointed out, combining information is only the second half of the problem, the first half being the problem of measurement. Although there are a large number of objective measurement devices currently in use in clinical psychology, it is doubtful that the clinician will be supplanted in the near future as a primary source of information used in prediction. Therefore, development of greater accuracy of clinical judgments is of fundamenPreparation of this article was carried out while Douglas H.

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“…Such reluctance may also explain why previous research observed that a majority of physicians do not use relevant statistical information properly in diagnostic inference. Casscells, Schoenberger, and Grayboys (1978), for instance, asked 60 house officers, students, and physicians at the Harvard Medical School to estimate the probability of an unnamed disease given the following information:If a test to detect a disease whose prevalence is 1/1,000 has a false positive rate of 5 per cent, what is the chance that a person found to have a positive result actually has the disease, assuming that you know nothing about the person's symptoms or signs? (p. 999) The estimates varied wildly, from the most frequent estimate, 95% (27 out of 60), down to 2% (11 out of 60).…”

mentioning

confidence: 99%

“…The value of 2% is obtained by inserting the problem information into Bayes' rule (see below)-assuming that the sensitivity of the test, which is not specified in the problem, is approximately 100%. Casscells et al (1978) concluded that "(...) in this group of students and physicians, formal decision analysis was almost entirely unknown and even common-sense reasoning about the interpretation of laboratory data was uncommon" (p. 1000). …”

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confidence: 99%

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How to Improve the Diagnostic Inferences of Medical Experts

Hoffrage

Gigerenzer

Experts in Science and Society

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Women are generally informed that mammography screening reduces the risk of dying from breast cancer by 25%. Does that mean that from 100 women who participate in screening, 25 lives will be saved? Although many people believe this to be the case, the conclusion is not justified. This figure means that from 1,000 women who participate in screening, 3 will die from breast cancer within 10 years, whereas from 1,000 women who do not participate, 4 will die. The difference between 4 and 3 is the 25% "relative risk reduction." Expressed as an "absolute risk reduction," however, this means that the benefit is 1 in 1,000, that is, 0.1%. Cancer organizations and health departments continue to inform women of the relative risk reduction, which gives a higher number-25% as compared to 0.1%-and makes the benefit of screening appear larger than if it were represented in absolute risks.The topic of this chapter is the representation of information on medical risks. As the case of mammography screening illustrates, the same information can be presented in various ways. The general point is that information always requires representation, and the choice between alternative representations can influence the patients' willingness to participate in screening, or more generally, patients' understanding of risks and choices of medical treatments. The ideal of "informed consent" can only be achieved if the patient knows about the pros and cons of a treatment, or the chances that a particular diagnosis is right or wrong. However, in order to communicate such uncertainties to the patients, the physician has to first understand statistical information and its implications. This requirement sharply contrasts with the fact that physicians are rarely trained in risk communication, and some still think that medicine can dispense with statistics and psychology. Such reluctance may also explain why previous research observed that a majority of physicians do not use relevant statistical information properly in diagnostic inference. Casscells, Schoenberger, and Grayboys (1978), for instance, asked 60 house officers, students, and physicians at the Harvard Medical School to estimate the probability of an unnamed disease given the following information:If a test to detect a disease whose prevalence is 1/1,000 has a false positive rate of 5 per cent, what is the chance that a person found to have a positive result actually has the disease, assuming that you know nothing about the person's symptoms or signs? (p. 999) The estimates varied wildly, from the most frequent estimate, 95% (27 out of 60), down to 2% (11 out of 60). The value of 2% is obtained by inserting the problem information into Bayes' rule (see below)-assuming that the sensitivity of the test, which is not specified in the problem, is approximately 100%. Casscells et al. (1978) concluded that "(...) in this group of students and physicians, formal decision analysis was almost entirely unknown and even common-sense reasoning about the interpretation of laboratory data was uncommon...

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Interpretation by Physicians of Clinical Laboratory Results

Cited by 665 publications

References 16 publications

How to improve Bayesian reasoning without instruction: Frequency formats.

How to improve Bayesian reasoning without instruction: Frequency formats.

Reducing the dependence of clinical judgment on the immediate context: Effects of number of categories and type of anchors.

How to Improve the Diagnostic Inferences of Medical Experts

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