Research Productivity among Plastic Surgeons in the State of Israel: h-index and M-quotient Assessment
Bibliographic record
Abstract
Background: The h-index has been proven in the US and Canada to be a solid tool to assess the quality and impact of individual scientific work in the field of plastic surgery. M-quotient is an additional metric that mitigates the h-index’s inherent bias toward more seasoned researchers. The objective of this study was evaluating the relationship between h-index and M-quotient and research productivity among plastic surgeons in the state of Israel. Methods: A list of all Israeli board-certified plastic surgeons registered in the Israeli Society of Plastic and Aesthetic Surgery was obtained from the organization’s website. Relevant demographic and academic factors of each surgeon were retrieved. The Scopus database was queried to determine each surgeon’s h-index and M-quotient, among other bibliometric parameters. Results: Our study included 173 plastic surgeons, 90% of whom were men. In total, 49.7% were working in academically affiliated hospitals; 14.4% of the surgeons had an academic rank. The mean h-index was 6.13; mean M-quotient was 0.27. Statistical analysis demonstrated a positive correlation between total number of publications (P < 0.0001), total number of citations (P < 0.0001), the surgeon’s seniority (P < 0.0001), academic rank (P = 0.007), appointed as past/present plastic surgery department director (P < 0.0001), and working in an academic affiliated hospital (P < 0.025). The same parameters were found to have a positive correlation with M-quotient. Conclusions: The h-index is an effective measure to compare plastic surgeons’ research productivity in Israel. M-quotient is an ancillary tool for the assessment of research productivity among plastic surgeons, with the advent of neutralizing the surgeon’s seniority.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".