Failure to disclose industry funding impacts outcomes in randomized controlled trials of platelet‐rich plasma
Bibliographic record
Abstract
PURPOSE: Platelet-rich plasma (PRP) represents a highly profitable biological therapy. Platelet-rich plasma is widely used to treat musculoskeletal disorders despite mixed evidence of its efficacy. As evidenced by literature from other domains, industry funding may influence the results of clinical trials. The objective of the current study was to determine the association between industry funding and positive results for randomized controlled trials (RCTs) assessing the efficacy of PRP in musculoskeletal disorders. METHODS: A search of four databases was conducted. Included studies were RCTs comparing PRP to any non-PRP comparator in adults (18 years old or over) with musculoskeletal disorders and had full text available in English. Studies were excluded if they were published before 2016 or were non-human trials. A multivariate binomial logistic regression model was created to explore predictors of statistically significant findings. Covariates included the presence of industry funding, sample size, and length of study follow-up. 1440 records were screened with 87 trials included in the final analysis. RESULTS: Of the 87 studies, 61 (70%) reported a statistically significant primary outcome. The presence of industry funding was not predictive of a statistically significant primary outcome [OR = 0.36, 95% CI 0.096-1.36, (n.s.)]. Studies that did not state whether industry funding was present had a higher chance of reporting a statistically significant primary outcome (OR = 3.61, 95% CI 1.1-11.9, p = 0.035). Sample size and length of follow-up were not predictive of a statistically significant primary outcome. CONCLUSION: The results of the current study conclude that industry funding had no impact on the reporting of positive results for RCTs investigating PRP in musculoskeletal disorders. However, not disclosing sources of funding was associated with a higher likelihood of reporting positive results. The results of trials that fail to disclose funding sources should be interpreted with caution in the PRP literature. LEVEL OF EVIDENCE: I.
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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 | 0.546 | 0.852 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.017 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.008 | 0.005 |
| 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".