Scripts and clinical reasoning
Bibliographic record
Abstract
CONTEXT: Each clinical encounter represents an amazing series of psychological events: perceiving the features of the situation; quickly accessing relevant hypotheses; checking for signs and symptoms that confirm or rule out competing hypotheses, and using related knowledge to guide appropriate investigations and treatment. OBJECTIVE: Script theory, issued from cognitive psychology, provides explanations of how these events are mentally processed. This essay is aimed at clinical teachers who are interested in basic sciences of education. It describes the script concept and how it applies in medicine via the concept of the 'illness script'. METHODS: Script theory asserts that, to give meaning to a new situation in our environment, we use goal-directed knowledge structures adapted to perform tasks efficiently. These integrated networks of prior knowledge lead to expectations, as well as to inferences and actions. Expectations and actions embedded in scripts allow subjects to make predictions about features that may or may not be encountered in a situation, to check these features in order to adequately interpret (classify) the situation, and to act appropriately. CONCLUSIONS: Theory raises questions about how illness scripts develop and are refined with clinical experience. It also provides a framework to assist their acquisition.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".