Predictors of Citations for Original Research in Ophthalmology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.142 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it