Association between industry support and the reporting of study outcomes in randomized clinical trials of dental implant research from the past 20 years
Bibliographic record
Abstract
BACKGROUND: Industry support is a significant funding source in implant dentistry research, not only to provide regulatory processes, but also to validate and promote products through randomized clinical trials (RCTs). However, industry funding should not affect scientific outcomes. PURPOSE: The aim of this study was to investigate whether there is an association between industry support for RCTs in implant dentistry and a greater chance of the reporting of positive outcomes, and whether there are other funding tendencies. MATERIALS AND METHODS: Randomized clinical trials from five implant dentistry journals were reviewed. Data were extracted, and descriptive and inferential statistical analyses (α = 0.05), including bivariate and multivariable logistic regression, and Spearman's correlation were performed. RESULTS: Two hundred eleven RCTs were included. Industry-funded and -unfunded studies presented similar outcomes, in terms of positive and negative results (p ≥ 0.05). North American and European countries received more industry funding, as did high-income countries, which showed well-established collaboration with each other. Clinical Oral Implants Research and Clinical Implant Dentistry and Related Research published 83.6% of industry-funded articles. Industry-funded studies from middle-income countries established more international collaborations with high-income countries than did unfunded studies. Citation numbers were similar for funded and unfunded studies. The chance of RCTs being industry-funded was higher for high-income (odds ratio [OR] = 3.00; 95% confidence interval [CI], 0.99-9.32; p = 0.05) and North American articles (OR = 3.40; 95% CI, 1.37-8.42; p = 0.008) than in lower-middle-income and other continents, respectively. Higher industry funding was associated with specific topics such as "surgical procedures," "prosthodontics topics," and "implant macrodesign" (OR = 4.7; 95% CI, 1.45-15.20; p = 0.010) and with the increase in numbers of institutions (OR = 1.52; 95% CI, 1.16-2.0; p = 0.002). CONCLUSION: The available evidence suggests no association between industry funding and greater chances of the reporting of positive outcomes in implant dentistry RCTs. A strong association was identified in industry trends concerning geographic origins, higher numbers of institutions, and specific research topics.
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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 | 0.479 | 0.827 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.010 | 0.019 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".