Reporting disclosures to the reader in plastic surgery journal publications
Bibliographic record
Abstract
Background With the associations between investigators and funding sources becoming increasingly complicated, conflicts of interest may arise that could potentially cause biases in the reporting of results. Objective To determine the number of published plastic surgery articles that lack reporting of disclosures. Methods An online review of four major North American plastic surgery journal publications from January 1, 2007 to December 31, 2007, was performed. For identification and to provide anonymity, journals were assigned a letter from A to D. Results Of the 1759 articles reviewed, 726 (41%) were included. Disclosure was not reported in 368 (51%) articles: Journal A (n=10, 3%), Journal B (n=153, 85%), Journal C (n=193, 93%) and Journal D (n=12, 32%). Journals differed significantly in their reporting of disclosure (P<0.01). Conclusion In the plastic surgery journals reviewed, the lack of documentation of disclosures was frequent. To ensure identification of bias in plastic surgery publications, a section dedicated to disclosure statements is recommended for each published 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 | 0.125 | 0.620 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".