Natalizumab Induces Sustained Response and Remission in the Absence of Concomitant Immunosuppressants in Patients with Crohnʼs Disease Who Failed Prior Anti-TNFα Therapy
Bibliographic record
Abstract
Purpose: This post-hoc analysis assessed the need for concomitant immunosuppressants (IMM) for induction and maintenance of response and remission with natalizumab (NAT) in patients (pts) who failed prior anti-tumor necrosis factor α (TNFα) therapy. Methods: In the ENCORE induction trial, 509 pts with CDAI scores ≥220 and ≤450 and CRP levels > 2.87 mg/L were randomized 1:1 to receive NAT (N = 259) or placebo (PBO; N = 250) at Mths 0, 1 and 2. In the ENACT2 maintenance trial, NAT-treated pts who had responded in ENACT1 and had a CDAI score <220 were re-randomized 1:1 to receive monthly NAT (N = 168) or PBO (N = 171) for up to 12 mths. Results: In ENCORE, 54 NAT- and 51 PBO-treated pts failed prior anti-TNFα therapy and did not receive IMM at baseline. Within this subgroup a significantly greater proportion of NAT-treated pts were in clinical response at Mths 2 and 3 and at both timepoints combined, compared to PBO (Table 1). A significantly greater proportion of NAT-treated pts were in clinical remission at Mth 3 and sustained remission through Mths 2 and 3 compared to PBO (Table 1). In ENACT2 21 NAT- and 19 PBO-treated pts failed prior anti-TNFα therapy and did not receive IMM at baseline. NAT treatment resulted in more pts in clinical response and remission throughout the study compared to PBO (Table 2). Significant differences in response and remission were observed, respectively, at Mths 9 through 12 and at Mths 6 through 12 (Table 2).Table 1Table 2Conclusion: Analyses of these trials suggest that NAT was effective in inducing response and maintaining remission in CD pts who failed prior anti-TNFα therapy and did not receive concomitant IMM.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".