Methodological characteristics and treatment effect sizes in oral health randomised controlled trials: Is there a relationship? Protocol for a meta-epidemiological study
Bibliographic record
Abstract
INTRODUCTION: It is fundamental that randomised controlled trials (RCTs) are properly conducted in order to reach well-supported conclusions. However, there is emerging evidence that RCTs are subject to biases which can overestimate or underestimate the true treatment effect, due to flaws in the study design characteristics of such trials. The extent to which this holds true in oral health RCTs, which have some unique design characteristics compared to RCTs in other health fields, is unclear. As such, we aim to examine the empirical evidence quantifying the extent of bias associated with methodological and non-methodological characteristics in oral health RCTs. METHODS AND ANALYSIS: We plan to perform a meta-epidemiological study, where a sample size of 60 meta-analyses (MAs) including approximately 600 RCTs will be selected. The MAs will be randomly obtained from the Oral Health Database of Systematic Reviews using a random number table; and will be considered for inclusion if they include a minimum of five RCTs, and examine a therapeutic intervention related to one of the recognised dental specialties. RCTs identified in selected MAs will be subsequently included if their study design includes a comparison between an intervention group and a placebo group or another intervention group. Data will be extracted from selected trials included in MAs based on a number of methodological and non-methodological characteristics. Moreover, the risk of bias will be assessed using the Cochrane Risk of Bias tool. Effect size estimates and measures of variability for the main outcome will be extracted from each RCT included in selected MAs, and a two-level analysis will be conducted using a meta-meta-analytic approach with a random effects model to allow for intra-MA and inter-MA heterogeneity. ETHICS AND DISSEMINATION: The intended audiences of the findings will include dental clinicians, oral health researchers, policymakers and graduate students. The aforementioned will be introduced to the findings through workshops, seminars, round table discussions and targeted individual meetings. Other opportunities for knowledge transfer will be pursued such as key dental conferences. Finally, the results will be published as a scientific report in a dental peer-reviewed journal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | MetaresearchMeta-epidemiology (broad) Domain: Methods · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | low |
| gpt | MetaresearchMeta-epidemiology (narrow)Meta-epidemiology (broad) Domain: Methods · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | high |
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 | 0.202 | 0.424 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.022 | 0.033 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.018 | 0.020 |
| Insufficient payload (model declined to judge) | 0.054 | 0.011 |
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.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".