Duplicate publications: A sample of redundancy in the Journal of Urology
Bibliographic record
Abstract
PURPOSE: : Redundant publications occur when authors publish a partial or complete duplicate of data from an existing manuscript. The push for academic advancement in medicine may result in redundant publications that erode the quality of literature. We sampled the extent of redundancy within the Journal of Urology. METHODS: : Original articles published in the Journal of Urology in 2006 were reviewed. MEDLINE was used to identify suspected duplicate publications by combining the last names of the first, second and last authors with keywords provided by the article. Results were limited to 2004 to 2008. Two investigators reviewed the suspected duplicate publications and classified them as duplicate, probable duplicate and salami-slicing. RESULTS: : We screened 723 original articles. Of these original articles, 13 (1.8%) had some form of redundancy. One (0.1%) original article had a duplicate article, 5 (0.7%) original articles had probable duplicates, and 7 (1%) original articles were salami-sliced. The proportion of redundant articles published prior to, and following, their 2006 index article was 5/13 (38.5%) and 7/13 (53.8%), respectively. One duplicate (7.7%) was published in the same month as its index. CONCLUSION: : Detection of redundant publications is a laborious process for reviewers and editors. This sampling of the Journal of Urology revealed that the duplication rate in this journal is small, but significant. Further assessment of the urological literature is warranted.
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 | 0.057 | 0.259 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.031 | 0.024 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| 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".