Commentary on: Simulation: An Effective Method of Teaching Cosmetic Botulinum Toxin Injection Technique
Bibliographic record
Abstract
The authors present a novel study investigating the utility of simulation for teaching skills associated with the injection of facial neurotoxin.1 For those less acquainted with the currently evolving educational paradigm, simulation training is now recognized to play a fundamental role in the training of the next generations of plastic surgeons as competency-based education is progressively adopted by plastic surgery residency training programs across North America.2 As a specialty, however, plastic surgery has been slow to adopt simulation, specifically in the subspecialty area of aesthetic surgery and procedures3 in which educators struggle to provide adequate hands-on pedagogical exposure for residents due to patient expectations and reluctance to participate in training.4,5 A recent survey investigating the quality of training in plastic surgery showed that 53.7% of residents felt they were least trained in aesthetic surgery.6 For these reasons, the present contribution on simulation training for facial injections by the authors is a welcome one.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.068 | 0.042 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".