Industry and evidence-based medicine: Believable or conflicted? A systematic review of the surgical literature
Bibliographic record
Abstract
BACKGROUND: Over the last few decades medical research and development has come to depend more heavily on the financial support of industry. However, there is concern that financial relations between the medical community and medical industry could unduly influence medical research and therefore patient care. Our objective was to determine whether conflict of interest owing to authors'/investigators' financial affiliation with industry associated with their academic research has been identified in the surgical literature. In particular, we sought to answer the following questions: What is the extent of such conflict of interest? Does conflict of interest bias the results of academic surgical research in favour of industry? What are the potential causes of this proindustry bias? METHODS: We conducted a systematic review of the literature in May 2008 using the OVID SP search engine of MEDLINE, EMBASE, CINAHL, the Cochrane Database of Systematic Reviews, DARE and Health Technology Assessment. Quantitative studies that included a methods section and reported on conflict of interest as a result of industry funding in surgery-related research specifically were included in our analysis. RESULTS: The search identified 190 studies that met our criteria. Author/investigator conflict of interest owing to financial affiliation with industry associated with their academic research is well documented in the surgical literature. Six studies demonstrated that authors with such conflicts of interest were significantly more likely to report a positive outcome than authors without industry funding, which demonstrates a proindustry bias. Two studies found that the proindustry bias could not be explained by variations in study quality or sample size. CONCLUSION: The conflict of interest that exists when surgical research is sponsored by industry is a genuine concern.
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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 | 0.144 | 0.479 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.037 | 0.035 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| 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".