How to Use an Article Reporting a Multiple Treatment Comparison Meta-analysis
Bibliographic record
Abstract
Multiple treatment comparison (MTC) meta-analysis uses both direct (head-to-head) randomized clinical trial (RCT) evidence as well as indirect evidence from RCTs to compare the relative effectiveness of all included interventions. The methodological quality of MTCs may be difficult for clinicians to interpret because the number of interventions evaluated may be large and the methodological approaches may be complex. Clinicians and others evaluating an MTC should be aware of the potential biases that can affect the interpretation of these analyses. Readers should consider whether the primary studies are sufficiently homogeneous to combine; whether the different interventions are sufficiently similar in their populations, study designs, and outcomes; and whether the direct evidence is sufficiently similar to the indirect evidence to consider combining. This article uses the existing Users' Guides format to address study validity, interpretation of results, and application to a patient scenario.
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
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 | 0.075 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; both teacher heads 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".