The 100 Most-Cited Articles on Optic Neuritis: Trends of Subtypes, Authorship, and Time
Bibliographic record
Abstract
BACKGROUND: Optic neuritis (ON) is an optic nerve inflammation that may lead to different degrees of vision loss. In recent decades, ON research facilitated a better understanding of the disease and its subtypes. This bibliometric analysis aimed to detect the 100 most-cited medical articles related to ON in the last 50 years (1972-2021) and describe publication trends arising from the list. METHODS: The Scopus database was used to locate and screen the 100 most influential ON papers based on the number of citations per article. Each entry was reviewed for the first author (name, gender, institution, and country), year of publication, journal, number of citations, ON subtype, and study design. The mean impact factor (IF) of each journal was calculated. RESULTS: The median number of citations was 265 (range 182-2,396). Observational studies on neuromyelitis optica-associated ON were the most common (27%), and the most influential decade was 2002-2011 (54 papers). Seventy-nine percent of articles were published in neurology journals, and a positive correlation between the mean number of citations per article and the journal mean IF was observed ( r = 0.62, P < 0.001). Between 2009 and 2021, female authors led more ON studies (52%), and more publications originated outside the USA (68%), compared with previous years. CONCLUSIONS: This analysis summarizes the impact and shifting trends of ON research in the last decades.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.082 | 0.105 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".