Innovatively Bridging Gaps in Aesthetic Surgery Training: Insights and Initiatives
Bibliographic record
Abstract
Worldwide, studies have consistently pointed out deficiencies in aesthetic surgery training due to a lack of structured training programs. In India, residents lack confidence in cosmetic surgery procedures posttraining, primarily due to limited exposure to aesthetic surgery procedures in teaching hospitals.[1] A comparative survey of aesthetic training systems revealed that the combined theoretical and hands-on approach in System A (Brazil) resulted in higher self-confidence among junior plastic surgeons compared with the solely theoretical approach in System B (Italy).[2] Notably, Vissers et al[3] highlighted the contrast in plastic surgery training between the United Kingdom and Belgium, where Belgium's integrated aesthetic surgery training resulted in higher confidence levels; the UK's National Health Service lacked exposure to cosmetic surgery. A study in United States showed over half of residents felt least trained in aesthetic surgery, with 56.4% intending to seek additional training postresidency, especially those with more experience in specific subspecialties. However, there was increased confidence among residents, particularly Postgraduate Year-5 and Postgraduate Year-6, after participating in clinic rotations.[4] Residents in Europe are mandated to have aesthetic surgery exposure for board certification.[5] Residents in Canada showed an increasing number of aesthetic procedures performed as training progressed, with confidence levels rising throughout the residency period.[6]
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".