Ranking treatments in the network meta-analysis should consider the certainty of evidence
Bibliographic record
Abstract
In a network meta-analysis, Juan Lasa and colleagues 1 Lasa JS Olivera PA Danese S Peyrin-Biroulet L Efficacy and safety of biologics and small molecule drugs for patients with moderate-to-severe ulcerative colitis: a systematic review and network meta-analysis. Lancet Gastroenterol Hepatol. 2022; 7: 161-170 Google Scholar compared biologics and small molecule drugs for the treatment of patients with moderate-to-severe ulcerative colitis and used the surface under the cumulative ranking (SUCRA) method to rank the agents. Based on the SUCRA scores, the authors found that upadacitinib ranked the highest for the induction of clinical remission (SUCRA 0·996) and concluded that upadacitinib was the best performing agent for induction of clinical remission (the primary outcome). 1 Lasa JS Olivera PA Danese S Peyrin-Biroulet L Efficacy and safety of biologics and small molecule drugs for patients with moderate-to-severe ulcerative colitis: a systematic review and network meta-analysis. Lancet Gastroenterol Hepatol. 2022; 7: 161-170 Google Scholar However, when considering the limitations of SUCRA, this conclusion might be inappropriate. Ranking treatments in the network meta-analysis should consider the certainty of evidence – Authors' replyWe thank Meixuan Li and colleagues for their interest in our study1 and for highlighting an interesting topic regarding network meta-analyses and the ranking methods used in many of them. Full-Text PDF
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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 | 0.259 | 0.666 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.015 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.012 | 0.021 |
| Insufficient payload (model declined to judge) | 0.014 | 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, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier 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".