Bibliographic record
Abstract
BACKGROUND: Recent descriptions of journal clubs identify their purpose as reading current medical literature, critically appraising it for validity and applicability to the readers' patient population, and distilling the best available clinical evidence. A clinical problem or question from practice within a discipline is identified, and relevant literature is selected and critically appraised. The process addresses the first tenet of evidence-based medicine; that is, gathering the best evidence from research data, but there is little information about when and how the second and third tenets (namely, incorporating individual clinician's expertise and individual patient's perspective) are addressed. AIMS: The study aim was to explore the value, for physician-learners, of reading physician-authored books within the context of an ongoing conversation group. This paper draws on the results of a year-long study with a group of medical students, residents, and novice physicians who read physician-authored books about their practice areas and subsequently met in a conversation group. DESCRIPTION: The study process facilitated learning around two neglected tenets of evidence-based medicine: the integration of clinical expertise, and incorporating patients' perspectives into clinical decision-making. It also fulfilled an earlier purpose of journal clubs, namely the fostering of collegiality and the development of professional identity in physicians. CONCLUSION: This study shows the value of reading a type of medical literature that is different, but complementary, to the kind read in contemporary journal clubs.
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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 | 0.013 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.034 | 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".