Evaluating an International Facial Trauma Course for Surgeons: Did We Make a Difference?
Bibliographic record
Abstract
Study Design: Retrospective data analysis study. Objective: Attending continuing professional development (CPD) and continuing medical education (CME) activities is a necessity for practicing surgeons in most parts of the world. To enhance best practices in conducting CME/CPD, objective evaluation of these events is crucial. This article aims to evaluate one such international standardized CPD course conducted for facial surgeons across the globe. The Management of Facial Trauma course was developed by an international planning committee of experienced surgeons and has been implemented in all regions of the world. Method: This 2-day course is delivered using a combination of short lectures, small group discussions, and practical hands-on activities. Data collected from pre- and post-course evaluations of 86 Management of Facial Trauma courses conducted worldwide from 2017-2019 were collated and analyzed. Results: Participant demographics and experience levels varied slightly across the regions. Evaluation of the course effectiveness revealed overall high ratings for educational impact, content usefulness, and faculty performance. Conclusion: Our results indicated that this standardized course met the audience needs and enabled participants to plan changes in clinical practice. In addition, it confirmed that the course was relevant across different specialties and across different cultures and countries.
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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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".