Bibliographic record
Abstract
In the letter to the editor written by Bonovas, Peyrin-Biroulet, and Danese, the authors accurately recognized a data recording error in the manuscript entitled “The comparative effectiveness of mesalamine, sulfasalazine, corticosteroids, and budesonide for the induction of remission in Crohn's disease: A Bayesian network meta-analysis.”1 Accordingly, the manuscript has been revised to address this mistake, and erratum will be published. The source of the error originated from the Crohn's II randomized controlled trial (RCT); this study was reported in a previous meta-analysis because of the study being unpublished.2 The data were extracted from a forest plot in the manuscript (Fig. 2). The forest plot defined an event as “failure to achieve remission,”2 which was erroneously recorded as “achieving remission” (i.e., the inverse outcome) in our study.1 Consequently, all analyses performed in the manuscript were revised using the proper values recorded for Crohn's II RCT. In our network meta-analysis, the comparison between high-dose mesalamine and placebo changed to an odds ratio of 1.86 (95% credible interval: 1.14–3.14) in the updated version. In the traditional meta-analysis, using only the direct head-to-head RCTs, in comparing high-dose mesalamine with placebo, the odds ratio dropped to 1.55 (95% credible interval: 0.80–3.11) in the revised analysis. Thus, the direct treatment effect comparing high-dose mesalamine with placebo was no longer statistically significant, whereas the estimate from the network meta-analysis remained significant. Network meta-analyses have the advantage of borrowing the strength of indirect evidence and can improve the precision of estimates. Although they rely on the assumption that the effect modifiers are balanced across comparisons, we did not detect any statistical inconsistency between the direct and indirect evidence in our node-splitting analyses.3 The differences of our network meta-analysis1 as compared to a previously published network meta-analysis4 highlight that the results of network meta-analyses are dependent on the definitions of variables and the selection of RCTs. The uncertainty rising from differences in network meta-analyses is a justification for the design of a well-powered RCT in patients with mild to moderate Crohn's disease using strict definitions of remissions.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.055 | 0.043 |
| Insufficient payload (model declined to judge) | 0.014 | 0.011 |
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".