Extent, transparency and impact of industry funding for pelvic mesh research: a review of the literature
Bibliographic record
Abstract
BACKGROUND: Conflicts of interest inherent in industry funding can bias medical research methods, outcomes, reporting and clinical applications. This study explored the extent of funding provided to American physician researchers studying surgical mesh used to treat uterine prolapse or stress urinary incontinence, and whether that funding was declared by researchers or influenced the ethical integrity of resulting publications in peer reviewed journals. METHODS: Publications identified via a Pubmed search (2014-2021) of the terms mesh and pelvic organ prolapse or stress urinary incontinence and with at least one US physician author were reviewed. Using the CMS Open Payments database industry funding received by those MDs in the year before, of and after publication was recorded, as were each study's declarations of funding and 14 quality measures. RESULTS: Fifty-three of the 56 studies reviewed had at least one American MD author who received industry funding in the year of, or one year before or after publication. For 47 articles this funding was not declared. Of 247 physician authors, 60% received > $100 while 13% received $100,000-$1,000,000 of which approximately 60% was undeclared. While 57% of the studies reviewed explicitly concluded that mesh was safe, only 39% of outcomes supported this. Neither the quality indicator of follow-up duration nor overall statements as to mesh safety varied with declaration status. CONCLUSIONS: Journal editors' guidelines re declaring conflicts of interest are not being followed. Financial involvement of industry in mesh research is extensive, often undeclared, and may shape the quality of, and conclusions drawn, resulting in overstated benefit and overuse of pelvic mesh in clinical practice.
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 | 0.065 | 0.207 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.029 | 0.026 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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, 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".