Incorporating dose effects in network meta-analysis
Bibliographic record
Abstract
Systematic reviews with network meta-analysis that ignore potential dose effects could limit the applicability and validity of review findings. This article aims to help content experts (eg, clinicians), methodologists, and statisticians better understand how to incorporate dose effects in network meta-analysis. Three models are described that make different clinical and statistical assumptions about how to model dose effects. This article also illustrates the importance of dose effects in understanding the potential risk of harm in people with dementia from cerebrovascular events associated with atypical antipsychotic drug use (quetiapine, olanzapine, and risperidone) and the potential risk of harm in people with nausea and headache associated with cholinesterase inhibitor use (donepezil, galantamine, and rivastigmine). Finally, important considerations when choosing between different network meta-analysis models incorporating dose effects are discussed.
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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 | 0.164 | 0.370 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.036 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".