Science is fundamental: the role of biomedical knowledge in clinical reasoning
Bibliographic record
Abstract
CONTEXT: Although training in basic science is generally considered a critical aspect of medical education, there is little consensus regarding its precise role in clinical reasoning. Whereas some reports suggest that biomedical knowledge is rarely used in routine diagnosis, other research has found that biomedical knowledge can become an integral part of the expert knowledge base. OBJECTIVE: The purpose of the current paper is to present evidence in support of different views regarding the role of biomedical knowledge, including the two-world hypothesis, encapsulation theory and recent work on the role of biomedical knowledge in novice diagnosticians. The implications of these models for clinical teaching will be examined. DISCUSSION: Recent work suggests that biomedical knowledge can help novices develop a coherent and stable mental representation of disease categories. As a result, learners are able to retain clinical knowledge over time and maintain diagnostic accuracy when faced with clinical challenges. This suggests that clinical teachers should attempt to make explicit connections between biomedical knowledge and clinical facts during training.
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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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.014 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".