Assessment of Conflicts of Interest in Robotic Surgical Studies
Bibliographic record
Abstract
BACKGROUND: Accurate conflict of interest (COI) statements are important, as a known COI may invalidate study results due to the potential risk of bias. OBJECTIVE: To determine the accuracy of self-declared COI statements in robotic studies and identify risk factors for undeclared payments. METHODS: Robotic surgery studies were identified through EMBASE and MEDLINE and included if published in 2015 and had at least one American author. Undeclared COI were determined by comparing the author's declared COI with industry reported payments found in the "Open Payments" database for 2013 and 2014. Undeclared payments and discrepancies in the COI statement were determined. Risk factors were assessed for an association with undeclared payments at the author and study level. RESULTS: A total of 458 studies (2253 authors) were included. Approximately, 240 (52%) studies had 1 or more author receive undeclared payments and included 183 where "no COI" was explicitly declared, and 57 with no declaration statement present. Moreover, 21% of studies and 18% of authors with a COI declared it so in a COI statement. Studies that had undeclared payments from Intuitive were more likely to recommend robotic surgery compared with those that declared funding (odds ratio 4.29, 95% confidence interval 2.55-7.21). CONCLUSIONS: We found that it was common for payments from Intuitive to be undeclared in robotic surgery articles. Mechanisms for accountability in COI reporting need to be put into place by journals to achieve appropriate transparency to those reading the journal article.
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 | 0.385 | 0.695 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.026 | 0.021 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".