Multiple treatment comparison meta-analyses: a step forward into complexity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The use of meta-analysis has become increasingly useful for clinical and policy decision making. A recent development in meta-analysis, multiple treatment comparison (MTC) meta-analysis, provides inferences on the comparative effectiveness of interventions that may have never been directly evaluated in clinical trials. This new approach may be confusing for clinicians and methodologists and raises specific challenges relevant to certain areas of medicine. This article addresses the methodological concepts of MTC meta-analysis, including issues of heterogeneity, choice of model, and adequacy of sample sizes. We address domain-specific challenges relevant to disciplines of medicine, including baseline risks of patient populations. We conclude that MTC meta-analysis is a useful tool in the context of comparative effectiveness and requires further study, as its utility and transparency will likely predict its uptake by the research and clinical community.
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.
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 | 0.354 | 0.490 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.028 | 0.018 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.036 | 0.016 |
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 it