Ranking treatments in the network meta-analysis should consider the certainty of evidence
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
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
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 | 0.045 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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