Non‐analytical models of clinical reasoning: the role of experience
Bibliographic record
Abstract
OBJECTIVE: This paper aims to summarise the evidence supporting the role of experience-based, non-analytic reasoning (NAR) or pattern recognition as a central feature of expert medical diagnosis. METHODS: The authors examine a series of studies, primarily from their own research programme at McMaster University, that demonstrate that expert and novice diagnostic problem solving is based, to some degree, on similarity to a prior specific exemplar in the memory. RESULTS: The studies reviewed have shown NAR to be a component of diagnostic reasoning at all levels from novice to subspecialist, and in dermatology, electrocardiography and psychiatry. The retrieval process is rapid and is not available to retrospect. It may be based on visual similarity, but can also be present in verbal descriptions. Some evidence exists that the process is unlikely to be available to introspection. Further, early hypotheses based on NAR can result in the re-interpretation of critical clinical findings. CONCLUSIONS: Non-analytic reasoning is a central component of diagnostic expertise at all levels. Clinical teaching should recognise the centrality of this process, and aim to both enhance the process through the learning of multiple examples and to supplement the process with analytical de-biasing strategies.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".