Predictors of Citations for Original Research in Ophthalmology
Bibliographic record
Abstract
There is a dearth of literature on factors associated with citation of publications in ophthalmology. We investigated predictors of citations for original ophthalmologic research articles based on author, study, and journal characteristics. In accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PRISMA), we extracted articles that studied the leading cause of vision impairment in the United States (cataract, diabetic retinopathy, age-related macular degeneration, and glaucoma) and were published in the top fifteen ophthalmology journals with the highest impact factors that accepted original research. Descriptive statistics, one-way analysis of variance (ANOVA) tests, and negative binomial regression were used to compare citation counts based on author, study, and journal characteristics. In this study, author research productivity, journal impact factor, study funding, and location in high-income countries were predictors of increased citation in ophthalmology.
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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 | 0.070 | 0.363 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.032 | 0.061 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".