Research productivity of ophthalmology residents in Saudi Arabia: A comprehensive analysis
Bibliographic record
Abstract
PURPOSE: The purpose of the study was to assess the research productivity of ophthalmology residents in Saudi Arabia and identify factors associated with significant research output. METHODS: This is a cross-sectional study of ophthalmology training programs across Saudi Arabia from 2015 to 2019. RESULTS: The study included 225 ophthalmology residents, with a mean number of publications of 3.25 ± 4.59 and an average h-index of 1.20 ± 1.38. Statistically significant differences were observed between training programs in the mean number of publications ( P < 0.001), h-index ( P < 0.001), and citations ( P < 0.001). The programs with the highest mean publication counts were King Saud University (8.03 ± 8.00), King Khaled Eye Specialist Hospital (3.80 ± 3.87), and the Eastern program (3.07 ± 4.04). Residents pursuing subspecialty fellowships, particularly in oculoplastic surgery and cornea, exhibited higher research output. Furthermore, the mean number of publications ( P < 0.001), citations ( P < 0.001), and h-index ( P < 0.001) were associated with attaining future academic positions. Pursuing fellowship was significantly linked to the number of publications ( P = 0.009) and h-index ( P = 0.012). CONCLUSION: This study underscores the variability in research productivity among ophthalmology residents and highlights the influence of training programs, fellowship ambitions, and academic aspirations on research output.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".