Characteristics of retracted articles in ophthalmology
Bibliographic record
Abstract
The retraction of publications is a crucial aspect of scientific integrity; it aims to correct the literature and alert scholars and the general public by identifying and labelling articles that contain erroneous data, unreliable findings, or flawed conclusions. Identifying and characterizing retracted articles within the scientific literature is thus very important. The aims of this article were to characterize retracted articles in the ophthalmological literature. One hundred and fifty-one retracted articles published between 1966 and 2023 were retrieved. The number of retracted articles showed an upward trend from 2020 onwards. Ocular oncology (n = 37, 24.5 %) was the most frequently represented subspeciality in the retracted articles, despite retina and uveitis being the most published. The most frequent reason for retraction was fake data (n = 62, 38 %). The labelling of retracted articles on some websites was unsatisfactory, especially on the free-access illegal platform Sci-Hub. On the other hand, platforms such as Dimensions, Scite and Retraction Watch exhibit promising accuracy. Improving the labelling of retractions is needed to reduce the citation of articles after they have been retracted. Solutions to reach this goal are discussed in this article.
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 | 0.013 | 0.175 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.027 | 0.024 |
| Science and technology studies | 0.002 | 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) | 0.006 | 0.002 |
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".