Ophthalmic Education: The Top 100 Cited Articles in Ophthalmology Journals
Bibliographic record
Abstract
Abstract Purpose To identify the top 100 (T100) cited articles on ophthalmic education and examine trends and areas of focus in ophthalmic education. Methods A literature search was conducted for articles published between 2011 and 2021 related to ophthalmic education within ophthalmology journals using the ISI Web of Science Core Collection database. The search was performed in June 2022 and was conducted using the search phrase ([educat* OR teach* OR instruct* OR train* OR “medical student*” OR residen* OR fellow* OR undergrad* OR postgrad* OR “faculty” OR “attending”] AND *ophthalm*). Results were analyzed using VOSviewer v.1.6.18 and statistical analysis was performed using Microsoft Excel. Results The majority of articles were published in the Journal of Cataract & Refractive Surgery (19%), followed by Ophthalmology (12%), and Eye (12%). Articles were most often published in the year 2013 (15%), followed by 2014 (12%) and 2012 (12%). Articles most commonly originated from English-speaking countries, including the United States (43%), England (14%), Canada (8%), and India (8%). Topics most often examined in ophthalmic education were resident education (51%), medical school education (21%), and surgical training (21%). The most common study types were cohort studies (22%), case series (21%), and prospective trials (16%). There were 16 institutions that produced more than one article in the T100 articles list. Conclusion The T100 articles on ophthalmic education were primarily U.S. based and focused on resident education, surgical training, and medical school ophthalmic curriculum. Further research into ophthalmic education is warranted to establish evidence-based curricula guidelines.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.100 | 0.103 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".