Conflict of interest reporting in dentistry meta-analyses: A systematic review
Bibliographic record
Abstract
OBJECTIVES: The issue of reporting conflicts of interest (COI) in medical research has come under scrutiny over the past decade. Absolute transparency is important when dealing with conflicts of interest to provide readers with all essential information required to make an informative decision of the results. The key objective of this study was to examine the prevalence of reporting conflicts of interest in therapeutic dental meta-analyses of Randomized Control Trials (RCTs), and to investigate possible associations with other categorical variables. STUDY DESIGN: We conducted an extensive literature search across multiple databases to search for relevant review articles for this study. We utilized pre-determined key words, and relied on three reviewers to test and review the use of a data extraction form that was used for the meta-analyses. Data regarding study characteristics, direction of results, and the significance of the results from each meta-analysis were extracted. RESULTS: There were 129 meta-analyses used in this review, and the reporting on conflict of interest was low with only 50 (38.8%) of the articles possessing a conflict of interest statement (either confirming of denying COI). Of these 50 articles, there were only 4 (8%) studies that reported an actual conflict of interest. A statement of conflicts of interest was found in 29 (35.3%) of the papers that reported significant findings, whereas 35% of the papers that reported positive results reported on conflict of interest. Prior to 2009, only 17 (25%) papers reported conflicts of interest, but since 2009, 54.1% of papers collected had a conflict of interest statement. CONCLUSIONS: Meta-analyses published in the field of dentistry do not routinely report author conflicts of interest. Although few conflicts appear to exist, the field of dentistry should continue to ensure that best evidence reports provide clear and transparent reporting of potential conflicts of interest in academic journals. Key words:Dentistry, dentition, meta-analysis, quantitative review.
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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 | 0.259 | 0.576 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.025 |
| Bibliometrics | 0.019 | 0.024 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".