Research Productivity and Impact of Saudi Academic Ophthalmologists: Trends in H-index, Sex, Subspecialty, and Faculty Appointments
Bibliographic record
Abstract
Objectives: This study aimed to assess the scientific output of academic ophthalmologists in the Kingdom of Saudi Arabia in terms of the H-index, sex, subspecialty, and faculty appointments. Methods: This cross-sectional study used data extracted from publicly available sources. Saudi academic ophthalmologists and their academic rankings were identified from their respective university websites. The H-indices were collected from the Scopus, Web of Science, and Google Scholar databases. Descriptive, univariate, and multivariate analyses were performed to explore the association of the H-index with sex, academic ranking, and subspecialty. Results: A total of 93 Saudi academic ophthalmologists were included in the study. Men comprised 77% of the academic positions and tended to have higher academic positions than women. The mean H-indices for men and women were 5.04±5.21 and 4.19±4.31, respectively (p=0.54). The mean H-indices of lecturers, assistant professors, associate professors, and professors were 1±0.1, 3.06±3, 7.7±68, and 10±10.25, respectively. The H-index had a positive correlation with a faculty appointment with an unadjusted beta coefficient for professors of 8.264 (95% CI, 5.967 to 10.560) (p<0.001). Ocular pathology and glaucoma were the highest in research productivity, with mean H-indices of 11±9.8 and 7.8±7.5, respectively. Compared with the most common specialties of the cornea and anterior segment, the H-index had a significantly positive correlation with glaucoma and ocular pathology subspecialties at 3.442 and 8.500 unadjusted beta coefficients, respectively (p=0.015 and p=0.004, respectively). The top three subspecialties with female underrepresentation were general ophthalmology, surgical retina, and glaucoma. Conclusion: This study provides insights into the research productivity of Saudi academic ophthalmologists. A high academic ranking was associated with high research productivity, as measured by the H-index. Gender variation was noted in the academic and subspecialty representations.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".