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]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".