Coordination of Analytic and Similarity-Based Processing Strategies and Expertise in Dermatological Diagnosis
Bibliographic record
Abstract
BACKGROUND: Medical diagnosis may be thought of as a categorization task. Research and theory in psychology as well as medical decision making indicate at least 2 processes by which this categorization task may be accomplished: (a) analytic processing, in which one makes explicit use of clinical features to reach a diagnosis, and (b) similarity-based processing, in which one makes use of past exemplars to reach a clinical diagnosis. Recent research indicates that these 2 processes are complementary. PURPOSE: We investigate the coordination of analytic and similarity-based processes in clinical decision making to examine if the relative reliance on these 2 processes is (a) amenable to instruction and (b) dependent on level of clinical experience. METHODS: The reliance of these 2 processes was indexed by the performance of 12 preclinical medical students on cases dichotomized as typical and atypical (analytic processing) and on cases dichotomized as similar or dissimilar to cases seen previously in a training phase (similarity-based processing). RESULTS: The results indicated that both processes are operative. Of particular interest was that preclinical medical students enhanced their performance by adopting a similarity-based strategy. This was especially so for atypical cases. These results are in contrast to residents, who enhanced their performance by adopting an analytic strategy. CONCLUSIONS: The relative reliance on analytic and similarity-based processes is amenable to instruction and dependent on expertise.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".