The 3 faces of clinical reasoning: Epistemological explorations of disparate error reduction strategies
Bibliographic record
Abstract
There is general consensus that clinical reasoning involves 2 stages: a rapid stage where 1 or more diagnostic hypotheses are advanced and a slower stage where these hypotheses are tested or confirmed. The rapid hypothesis generation stage is considered inaccessible for analysis or observation. Consequently, recent research on clinical reasoning has focused specifically on improving the accuracy of the slower, hypothesis confirmation stage. Three perspectives have developed in this line of research, and each proposes different error reduction strategies for clinical reasoning. This paper considers these 3 perspectives and examines the underlying assumptions. Additionally, this paper reviews the evidence, or lack of, behind each class of error reduction strategies. The first perspective takes an epidemiological stance, appealing to the benefits of incorporating population data and evidence-based medicine in every day clinical reasoning. The second builds on the heuristic and bias research programme, appealing to a special class of dual process reasoning models that theorizes a rapid error prone cognitive process for problem solving with a slower more logical cognitive process capable of correcting those errors. Finally, the third perspective borrows from an exemplar model of categorization that explicitly relates clinical knowledge and experience to diagnostic accuracy.
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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 | 0.105 | 0.128 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.018 | 0.006 |
| Science and technology studies | 0.008 | 0.103 |
| Scholarly communication | 0.020 | 0.045 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".