The utilization of educational resources published by the Thoracic Surgery Residents Association
Bibliographic record
Abstract
Objective The Thoracic Surgery Residents Association (TSRA) is a trainee-led cardiothoracic surgery organization in North America that has published a multitude of educational resources. However, the utilization of these resources remains unknown. Methods Surveys were constructed, pilot-tested, and emailed to 527 current cardiothoracic trainees (12 questions) and 780 former trainees who graduated between 2012 and 2019 (16 questions). The surveys assessed the utilization of TSRA educational resources in preparing for clinical practice as well as in-training and American Board of Thoracic Surgery (ABTS) certification examinations. Results A total of 143 (27%) current trainees and 180 (23%) recent graduates responded. A higher proportion of recent graduates compared with current trainees identified as male (84% vs 66%; P = .001) and graduated from 2- or 3-year traditional training programs (81% vs 41%; P < .001), compared with integrated 6-year (8% vs 49%; P < .001) or 4 + 3 (11% vs 10%; P = .82) pathways. Current trainees most commonly used TSRA resources to prepare for the in-training exam (75%) and operations (73%). Recent graduates most commonly used them to prepare for Oral and/or Written Board Exams (92%) and the in-training exam (89%). Among recent graduates who passed the ABTS Oral Board Exam on the first attempt, 82% (97/118) used TSRA resources to prepare, versus only 48% (25/52) of recent graduates who passed after multiple attempts, failed, have not taken the exam, or preferred not to answer ( P < .001). Conclusions Current cardiothoracic trainees and recent graduates have utilized TSRA educational resources extensively, including to prepare for in-training and ABTS Board examinations.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".