Bibliographic record
Abstract
In the 1950s, teaching programs focused on the biomedical sciences; clinical decisions were left to expert intuition rather than rational analysis. During the past few decades, efforts have been made to understand the mental strategies employed by physicians during clinical decision making, and the reasons for errors that result from human bounded rationality. This chapter describes the recent shift in approach to clinical reasoning and practice—from intuitive to scientific. Today, medical students are offered courses in epidemiology, evidence-based medicine and medical economics. They are taught to generate diagnostic hypotheses, suggest information that would support or refute these hypotheses, and apply Bayes’ theorem for diagnostic reasoning and evidence-based principles for treatment choices. The author believes, however, that medical education is still in a state of transition from the determinism of the biomedical sciences to the uncertainty of clinical practice. To overcome the intellectual and emotional barriers to this transition, medical students must come to terms with two apparently incompatible conceptions: the cause-effect descriptive approach based on deterministic thinking, and one that views clinical practice as consisting of prescriptive decisions based on probabilistic estimates. Students must accept that clinical uncertainty is pervasive. To this end, clinical preceptors should openly share their thought processes—and their doubts—in clinical training.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.031 | 0.014 |
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".