Assessment of the Academic Productivity of Plastic Surgeons in Saudi Arabia Using the h-index
Bibliographic record
Abstract
Background: Hirsch-index (or h-index ) is a bibliometric measure calculated for researchers based on number of publications and their citations. This study examined the h-index of board-certified plastic surgeons in Saudi Arabia and the different factors that may influence it. Method: In this cross-sectional study, an electronic questionnaire was sent to 156 board-certified plastic surgeons practicing in Saudi Arabia. Using their names, we conducted an online search on Scopus, Semantic scholar, and Google scholar to calculate their h-index . Bivariate and multiple regression analyses were conducted to determine the relationship of those factors with the index. Results: A total of 84 surgeons participated in this study, of whom 83.3% were men. Our sample scored a mean index of 1.7 and published a mean of 5 articles. More publications and a higher academic rank predicted a higher h-index , ( β = 0.79, P < 0.001) and ( β = 0.14, P 0.017), respectively. On the other end of the spectrum, the country of residency training ( P 0.33), the year of training completion ( P 0.95), attaining fellowship training ( P 0.95), the number of fellowships ( P 0.20), interest in research ( P 0.74), working in an academic hospital ( P 0.44), or attaining a higher degree ( P 0.61) were not significant independent predictors of the index. Conclusions: More publications and a higher rank predicted increased academic productivity among the plastic surgeons in Saudi Arabia. Despite its limitations, h-index is a useful measure that can be considered in promotions and applications to prestigious plastic surgery centers in adjunct to other factors.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchBibliometrics Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | BibliometricsMetaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".