The Impact of Corporate Payments on Robotic Surgery Research
Bibliographic record
Abstract
OBJECTIVE: To quantify the influence of financial conflict of interest (COI) payments on the reporting of clinical results for robotic surgery. DATA SOURCES AND STUDY SELECTION: A systematic search (Ovid MEDLINE databases) was conducted (May 2017) to identify randomized controlled trials (RCTs) and observational studies comparing the efficacy of the da Vinci robot on clinical outcomes. Financial COI data for authors (per study) were determined using open payments database. MAIN OUTCOMES AND MEASURES: Primary outcomes assessed were receipt of financial COI payments and overall conclusion reported between robotic versus comparative approach. Quality/risk of bias was assessed using Newcastle-Ottawa Scale (NOS)/Cochrane risk of bias tool. Disclosure discrepancies were also analyzed. DATA EXTRACTION AND SYNTHESIS: Study characteristics, surgical subspecialty, methodological assessment, reporting of disclosure statements, and study findings dual abstracted. The association of the amount of financial support received as a predictor of reporting positive findings associated robotic surgery was assessed at various cut-offs of dollar amount received by receiver operating curve (ROC). RESULTS: Thirty-three studies were included, 9 RCTs and 24 observational studies. There was a median, 111 patients (range 10 to 6420) across studies. A little more than half (17/33) had a conclusion statement reporting positive results in support of robotic surgery, with 48% (16/33) reporting results not in favor [equivocal: 12/33 (36%), negative: 4/33 (12%)]. Nearly all (91%) studies had authors who received financial COI payments, with a median of $3364.46 per study (range $9 to $1,775,378.03). ROC curve demonstrated that studies receiving greater than $9557.31 (cutpoint) were more likely to report positive robotic surgery results (sensitivity: 0.65, specificity: 0.81, area under the curve: 0.73). Studies with financial COI payment greater than this amount were more likely to report beneficial outcomes with robotic surgery [(78.57% vs 31.58%, P = 0.013) with an odds ratio of 2.07 (confidence interval: 0.47-3.67; P = 0.011)]. Overall, studies were high quality/low risk of bias [median NOS: 8 (range 5 to 9)]; Cochrane risk: "low risk" (9/9, 100%)]. CONCLUSION AND RELEVANCE: Financial COI sponsorship appears to be associated with a higher likelihood of studies reporting a benefit of robotic surgery. Our findings suggest a dollar amount where financial payments influence reported clinical results, a concept that challenges the current guidelines, which do not account for the amount of COI funding received.
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 | Research integrity Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Metaresearch Domain: Incentives · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.565 | 0.847 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.012 | 0.018 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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, 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".