Non‐analytical models of clinical reasoning: the role of experience
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.166 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it