Treatment sequence network meta-analysis in Crohn’s disease: a methodological case study
Bibliographic record
Abstract
OBJECTIVE: Several biologic therapies are available for the treatment of mild-to-moderate Crohn's disease (CD). This network meta-analysis (NMA) aimed to assess the comparative efficacy of ustekinumab, adalimumab, vedolizumab and infliximab in the maintenance of clinical response and remission after 1 year of treatment. METHODS: A systematic literature search was performed to identify relevant randomized controlled trials (RCTs). Key outcomes of interest were clinical response (CD activity index [CDAI] reduction of 100 points; CDAI-100) and remission (CDAI score under 150 points; CDAI < 150). A treatment sequence Bayesian NMA was conducted to account for the re-randomization of patients based on different clinical definitions, the lack of similarity of the common comparator for each trial and the full treatment pathway from the induction phase onwards. RESULTS: Thirteen RCTs were identified. Ustekinumab 90 mg q8w was associated with statistically significant improvement in clinical response relative to placebo and vedolizumab 300 mg. For clinical remission, ustekinumab 90 mg q8w was associated with statistically significant improvement relative to placebo and vedolizumab 300 mg q8w. Findings from sub-population analyses had similar results but were not statistically significant. CONCLUSIONS: The NMA suggest that ustekinumab is associated with the highest likelihood of reaching response or remission at 1 year compared with placebo, adalimumab and vedolizumab. Results should be interpreted with caution because this is a novel methodology; however, the treatment sequence analysis may be the most methodologically sound analysis to derive estimates of comparative efficacy in CD in the absence of head-to-head evidence.
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
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 | 0.213 | 0.383 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.026 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".