Reporting disclosures to the reader in plastic surgery journal publications
Bibliographic record
Abstract
T here is no denying the complex, yet necessary relationships that exist among investigators, academic institutions, funding agencies and the industries that advance biomedical research (1-3).Within these interactions, there may arise conflicts of interest (COI) regarding financial interests, or personal, professional, political and intellectual interests that can potentially cause biases in the reporting of results (1, 4-7).For these reasons, disclosures have become a crucial component in the publication of studies.Biomedical research publishers rely on the integrity of the author to prevent fraud (6,8), and this trust is dependent on and maintained by the transparency provided by the use of journal-specific disclosure forms (2,8) and by the authors abiding to the Declaration of Helsinki (9).Furthermore, an integral part of an editor's evaluation of a manuscript involves access to the disclosure(s).The need for access to disclosures also applies to readers when formulating their own interpretation of published results (10).Reporting of COI should be systematically applied in the surgical literature.With regard to the plastic surgery publishing community, the need for the transparency of disclosures from manuscript submission to final print has previously been addressed (2).While plastic surgery journals require authors to complete disclosure forms when submitting studies for publication, this information is not uniformly made available to the reader (11-14).The objective of the present study was to determine the frequency of the lack of disclosure statements in articles published in four North American plastic surgery 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 | 0.235 | 0.725 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".