Factors Affecting the Time to Publication in Ophthalmology Journals: A Comprehensive Bibliometric Analysis
Bibliographic record
Abstract
There are currently no available aids for authors when selecting ophthalmology journals to submit their manuscripts. We aim to provide comprehensive data on the duration from submission to various stages of the publication process and assess factors influencing time to publication in ophthalmology journals. A list of ophthalmology journals was obtained from the 2019 Web of Science Journal Citation Report. Journal characteristics, such as five-year impact factor, number of authors per article, journal type, and number of multi-institutional articles, were collected. The dates of submission, acceptance, electronic and print publication for all articles published in an ophthalmology journal in 2019 were determined. In total, 56 journals and 8835 research articles were included. Of these articles, 3591 (40.6%) were open access and 4837 (54.7%) were multi-institutional. In 2019, most publications came from the United States of America (n = 1973), China (n = 1069) and Germany (n = 602). Significant associations were found between various predictors and a reduced mean number of days from submission to electronic publication: increased journal five-year impact factor (p = .026), more authors (p = .028), publishing in a hybrid journal (both open-access and subscription articles) versus an open-access journal (p = .021), and a reduced proportion of multi-institutional articles in a journal (p = .030). There is a wide variation in the time to acceptance and publication in ophthalmology journals. Authors can expect a shorter time to publication when publishing in high-impact journals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.058 | 0.097 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".