The Policies for the Disclosure of Funding and Conflict of Interest in Surgery Journals: A Cross‐Sectional Survey
Bibliographic record
Abstract
BACKGROUND: Industry through its funding of research and through its relationships with study authors can influence the results of research. Most journals have policies for reporting funding and disclosing conflict of interest (COI) to mitigate the influence of industry on research. The objective of this study is to assess the policies of surgery journals for the reporting of funding and the disclosure of COI. METHODS: We described the prevalence and characteristics of funding and COI policies of journals indexed under "Surgery" in the Journal Citation Reports. We extracted data from publicly available information and through simulation of manuscript submission. RESULTS: Of the 186 eligible journals, 171 (92%) had policies for reporting of funding. None of the policies described procedures to deal with non-reporting or underreporting of funding. Of the 186 journals, 183 (99%) had a policy for disclosure of COI. All journals with a COI policy required disclosure of financial interest, while 96 (52%) required the disclosure of non-financial interests. Only 24 (13%) policies described how non-disclosure of COI affects the editorial process, and none described procedures to verify COI disclosure. Of the policies that required disclosing COI, 94 (51%) also required reporting the source of financial COI. CONCLUSIONS: Most journals have policies for reporting of funding and disclosure of financial COI. However, many do not have clear policies for disclosing non-financial COI. Major limitations in the policies include the lack of processes for the verification of disclosed interests and for dealing with underreporting of funding and of COI.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".