P.120 Conflict of interest in neurosurgery: an analysis of disclosure policies in neurosurgical journals
Bibliographic record
Abstract
Background: Industry funding of neurosurgery research is on the rise and this creates a conflict of interest (COI) with the potential to bias results. The reporting and handling of COI is impacted by the variation in policies and definitions between journals. In this study we sought to evaluate the prevalence and comprehensiveness of COI policies amongst leading neurosurgical journals. Methods: We conducted a cross-sectional study of publicly available online disclosure policies in the 20 highest-ranking neurosurgical journals, as determined by Google Scholar Metrics, in July of 2016. Results: Eighteen (89.5%) of the top neurosurgical journals included COI policy statements. Ten journals requested declaration of non-financial conflicts, while two journals defined a time period of interest for conflicts. Sixteen journals required declaration from the corresponding author, 13 from all authors, six from reviewers and five from editors. Five journals included COI declaration verification, management or enforcement. Journals with more comprehensive COI policies were significantly more likely to have higher h5-indices (p=0.003) and higher impact factors (p=0.01). Conclusions: In 2016, the majority of high-impact neurosurgical journals had publicly available COI disclosure policies. Policies varied substantially across neurosurgical journals; but COI comprehensiveness was associated with impact factor and h5-index. More comprehensive and consistent COI policies will facilitate increased transparency in neurosurgery research.
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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 | 0.012 | 0.148 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.012 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".