Developing, Maintaining, and Teaching Clinical Diagnostic Expertise
Bibliographic record
Abstract
Summary Understanding the process of expert clinical reasoning improves our ability to develop, practice, maintain, teach, and assess clinical diagnostic expertise. The dual process model describes a synergistic interplay of associative thinking and analytical reasoning. These complimentary processes facilitate the efficient abstraction of data from clinical presentations, the identification of key features, and the production of useful problem representations. These are compared unconsciously to prototypical cases stored in memory as illness scripts for a best match. A lack of a satisfactory match may stimulate a conscious, analytic analysis of discordance, ideally reducing bias and error and promoting further script development. An awareness of this process and the use of existing observation and assessment techniques can enable both the teaching and the assessment of clinical reasoning. Learners can also be taught to use these techniques to help develop self-assessment of clinical reasoning performance. Teaching and assessing clinical reasoning in others stimulates clinician teachers to reflect on their own clinical reasoning and practice, serving as an effective form of continuous professional learning.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".