P.013 Conflicts of interest in neurosurgical research - comparing voluntary physician disclosure to mandatory company data
Bibliographic record
Abstract
Background: Industry involvement in neurosurgical research is common, creating financial conflicts of interest (COIs). Most journals require voluntary disclosure of financial COIs. In 2013, the Sunshine Act (SA) was passed in the US, mandating industry disclosure of all payments to physicians. The accuracy of voluntary disclosure can now be determined by comparing voluntary author disclosure with industry data. Methods: We reviewed disclosure statements and calculated rates of voluntary disclosure in major neurosurgical journals before (2011) and after (2013) the Sunshine Act to determine if voluntary disclosure increased after its implementation. We then determined the accuracy of voluntary disclosure in 2013, comparing voluntary disclosure with industry disclosure on the Open Payments Database (OPD). Mean, median and range of industry payments to neurosurgeons were calculated Results: Voluntary disclosure significantly increased in JNS-Spine only (10.7% to 35.4%,p<0.001) after implementation of the SA. The average rate of non-disclosure in all journals studied was 38.3% (Range 33.8%-42.2%) $32,598,522.97 of industry payments were provided to 656 authors in the five-month period studied (Average $49,692.87/author) Conclusions: Voluntary COI disclosure in JNS- Spine increased after implementation of the Sunshine Act. Industry payments to physicians publishing in neurosurgery journals are common and rates of non-disclosure of COIs are high. The ethical implications of COIs and non-disclosure are discussed.
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 | 0.024 | 0.171 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".