A Comparison of Research Productivity Across Plastic Surgery Fellowship Directors
Bibliographic record
Abstract
BACKGROUND: Objective measures of research productivity depend on how frequently a publication is cited. Metrics such as the Hirsch index (h-index; total number of publications h that have at least h citations) allow for an objective measurement of the scientific impact of an author's publications. OBJECTIVES: The purpose of this study was to assess and compare the h-index among aesthetic plastic surgery fellowship directors to that of fellowship directors in craniofacial surgery and microsurgery. METHODS: We conducted a cross-sectional study of all fellowship directors in aesthetic surgery, craniofacial surgery, and microsurgery in the United States and Canada. The gathered data were categorized as bibliometric (h-index, i10-index, total number of publications, total number of citations, maximum citations for a single work, and number of self-citations) and demographic (gender, training background). Descriptive statistics were computed. RESULTS: The sample was composed of 30 aesthetic surgeons (93% male), 33 craniofacial surgeons (97% male), and 32 microsurgeons (94% male). The mean h-index was 13.7 for aesthetics, 16.9 for craniofacial, and 12.4 for microsurgery. There were no significant differences for any of the bibliometric measures between the three subspecialties, despite the fact that academic rank and years in practice were significantly different. CONCLUSIONS: As measured by the h-index, there is a high level of academic productivity among fellowship directors, regardless of subspecialty area. Unlike other plastic surgery subspecialties however, the h-index of aesthetic plastic surgeons is not correlated to academic rank, revealing a discrepancy between perceptions of aesthetic plastic surgery and its actual academic impact.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".