A Structured, Debate-Style Cardiothoracic Surgery Journal Club for Trainee Acquisition and Application of Seminal Literature
Bibliographic record
Abstract
INTRODUCTION: The acquisition of specialty-specific seminal literature and its application to daily, clinical patient-care decisions are critical components of clinical training. This structured, debate-style cardiothoracic surgery journal club module engages trainees in discussion of realistic patient scenarios, incorporating an extensive body of literature identified as the best evidence for the practice of cardiothoracic surgery. METHODS: We designed the structured, debate-style cardiothoracic surgery journal club and delivered it to University of Texas MD Anderson Cancer Center cardiothoracic surgery trainees. Overall assessment of knowledge acquisition consisted of both direct judging of debates by faculty facilitators and a year-end written test of trainee knowledge. Associated materials include guidelines and resources for faculty facilitators and trainees to prepare them for the journal club debate. Also included are cardiothoracic surgery patient cases, PowerPoint presentation slides, a debate score sheet, and multiple-choice knowledge tests with answer keys. RESULTS: Our structured, debate-style cardiothoracic surgery journal club is an effective educational intervention for cardiothoracic surgical trainees to gain practice in applying specialty-specific, literature-based evidence to particular patient problems. DISCUSSION: This resource may be used by course directors for surgery, for independent study by individuals planning to matriculate into surgical residencies, or as a review for those already in surgical training. Moreover, this curriculum can be delivered at other clinical training programs.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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".