Bibliographic record
Abstract
How medical students are taught physical examination (PE) skills appears to have changed little since the 1950s. Textbooks are organized according to organ systems and describe methods of eliciting and recording history and PE data using a routine format. In many medical schools, the preclinical teaching programs for clinical examination skills similarly emphasize an orderly collection of data. Teaching students to use diagnostic reasoning is postponed until students have learned history-taking and PE skills. The authors propose three modifications to this educational approach. First, rather than performing the clinical examination using a routine format, students should be encouraged to form diagnostic hypotheses early on while listening to the patient's narrative, and conduct the subsequent search for history and PE data in a reflective way in order to confirm or refute these hypotheses. Second, the authors propose that interviewing patients and conducting the PE be taught by one-on-one tutoring until students achieve mastery. Last, they suggest that the PE be guided not only by students' diagnostic hypotheses, but also by patients' expectations. These modifications are consistent with current trends in medical education that encourage a reflective practice and problem-based learning (PBL), and they also introduce medical students to the precepts of clinical reasoning. The authors suggest that challenging students to seek specific physical findings may increase the likelihood of detecting findings when they are present, and may transform patient interviewing and conducting the PE from routine activities into intellectually exciting experiences.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.021 | 0.022 |
| Insufficient payload (model declined to judge) | 0.025 | 0.023 |
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".