Reviewing the Reviewers Potential Financial Conflicts of Interest in Editorial Boards of Surgery Journals
Bibliographic record
Abstract
OBJECTIVE: To assess the prevalence, magnitude, and disclosure status of industry funding in editorial boards of surgery journals. SUMMARY OF BACKGROUND DATA: Financial COI can bias research. Although authors seeking to publish in peer-reviewed surgery journals are required to provide COI disclosures, editorial board members' COI disclosures are generally not disclosed to readers. METHODS: We present a cross-sectional analysis of industry funding to editorial board members of high-impact surgery journals. We reviewed top US-based surgery journals by impact factor to determine the presence of financial COI in members of each journal's editorial board. The prevalence and magnitude of COI was determined using 2018 industry reported payments found in the Centers for Medicare and Medicaid Services Open Payments database. Journal websites were also reviewed looking for the presence of editorial board disclosure statements. RESULTS: A total of 1002 names of editorial board members from the top 10 high-impact American surgery journals were identified. Of 688 individual physicians based in the USA, 452 (65.7%) were found to have received industry payments in 2018, totaling $21,916,503 with a median funding amount per physician of $1253 (interquartile range $156-$10,769). Funding levels varied by surgical specialty and journal. Editorial board disclosure information was found in only 3.3% of physicians. CONCLUSIONS: Industry funding to editorial board members of high impact surgery journals is prevalent and underreported. Mechanisms of disclosure for COI are needed at the editorial board level to provide readers full transparency. This would acknowledge this COI of editorial board members, and thereby attempt to potentially further reduce the risk of bias in editorial decisions.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchResearch integrity Domain: Evaluation · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | MetaresearchResearch integrity Domain: Evaluation · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.018 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".