Conflicts of Interest Among Authors of Systematic Reviews and Meta-analyses Investigating Interventions for Melanoma: Cross-sectional Literature Study
Bibliographic record
Abstract
BACKGROUND: Previous studies have highlighted the potential influence that industry relationships may have on the outcomes of medical research. OBJECTIVE: We aimed to determine the prevalence of author conflicts of interest (COIs) in systematic reviews focusing on melanoma interventions, as well as to determine whether the presence of these COIs were associated with an increased likelihood of reporting favorable results and conclusions. METHODS: This cross-sectional study included systematic reviews with or without meta-analyses focusing on interventions for melanoma. We searched MEDLINE and Embase for eligible systematic reviews published between September 1, 2016, and June 2, 2020. COI disclosures were cross-referenced with information from the CMS (Centers for Medicare & Medicaid Services) Open Payments database, Dollars for Profs, Google Patents, the United States Patent and Trademark Office, and previously published COI disclosure statements. Results were quantified using descriptive statistics, and relationships were evaluated by Fisher exact tests. RESULTS: Of the 23 systematic reviews included in our sample, 12 (52%) had at least one author with a COI. Of these 12 reviews, 7 (58%) reported narrative results favoring the treatment group and 9 (75%) reported conclusions favoring the treatment group. Of the 11 systematic reviews without a conflicted author, 4 (36%) reported results favoring the treatment group and 5 (45%) reported conclusions favoring the treatment group. We found no significant association between the presence of author COIs and the favorability of results (P=.53) or conclusions (P=.15). CONCLUSIONS: Author COIs did not appear to influence the outcomes of systematic reviews regarding melanoma interventions. Clinicians and other readers of dermatology literature should be cognizant of the influence that industry may have on the nature of reported outcomes, including those from systematic reviews and meta-analyses.
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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 | MetaresearchResearch integrity Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | MetaresearchResearch integrity Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".