[PUBLICATION TRENDS AND IMPACT OF OPHTHALMOLOGY DEPARTMENTS IN ISRAEL: SUMMARIZING OVER A DECADE OF RESEARCH WITH AN INTERNATIONAL PERSPECTIVE (2011-2021)].
Bibliographic record
Abstract
BACKGROUND: A focused bibliographic analysis of Israel's ophthalmology research productivity has never been performed. OBJECTIVES: To assess the research output of Israeli ophthalmology departments between the years 2011-2021 and compare to leading countries in the discipline. METHODS: The PubMed search engine was used to detect all medical articles published between the years 2011 and 2021, with at least one author from an Israeli ophthalmology department. Each entry was reviewed for: year of publication, journal, department, sub-specialty, and study design. Journals were ranked according to Scopus Cite Score. Research output and population data of 7 top-ranked countries in ophthalmology (USA, UK, Japan, Germany, Australia, Canada, China) were documented for comparisons. RESULTS: Overall, 1,919 publications were included. Total research output of Israeli ophthalmology departments significantly increased throughout the study period (R2=0.85, P<0.0001). Medical retina publications were the most prevalent (493 articles, 25.6%), and the most commonly utilized design was cohort (536 articles, 28%). Over a third of all articles (669) were published in Q1 ophthalmology journals, and 16% were published in the top-10 journals. Israel was ranked (1/8) and (3/8) in publications per population and physician, respectively, compared to the other assessed countries. CONCLUSIONS: Between 2011-2021, the research productivity of the Israeli ophthalmological community has constantly increased across all sub-specialties and in high-impact journals. Israel's relative contribution to the ophthalmic medical literature is noteworthy internationally.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.038 | 0.050 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".