Bibliographic record
Abstract
The multiple treatment comparison meta-analysis (MTC-MA) by Cahill et al 1 described in this issue2 focuses on pharmacological interventions for smoking cessation. This MTC-MA provides a reminder of how useful this methodological approach can be. The efficacy of treatments for tobacco dependence is an excellent question for MTC-MA as smoking is prevalent and is a critical risk factor for many diseases with high morbidity and mortality; there is clinical interest in the relative efficacy of pharmacological interventions to aid cessation and direct head-to-head comparisons between each pair of interventions do not exist. Comparisons not based on direct research evidence are referred to as ‘indirect comparisons’. MTC-MA, also known as network meta-analysis, creates a network in which each node is a different intervention. MTC-MA provides a measure of treatment effect between all pairs of possible interventions in the network, including both direct and indirect comparisons, based on the existing data from controlled pair-wise comparisons in clinical trials.3 Practical interest in indirect comparisons has led to the development of rigorous approaches to conducting MTC-MA, and they are appearing more frequently in the clinical literature. An excellent summary for clinicians of how to use MTC-MA has been published recently in JAMA .4 Awareness of the criteria to look …
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 | Metaresearch Domain: Methods · Genre: Editorial About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Editorial About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.144 | 0.241 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.029 | 0.009 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.109 | 0.003 |
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".