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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".