Outpatient Ulcerative Colitis Primary Anti-TNF Responders Receiving Adalimumab or Infliximab Maintenance Therapy Have Similar Rates of Secondary Loss of Response
Bibliographic record
Abstract
GOALS: To compare the proportion of secondary loss of response to adalimumab and infliximab during maintenance treatment of ulcerative colitis (UC) after primary response to induction therapy. BACKGROUND: The efficacy of anti-tumor necrosis factor-α (TNF-α) therapy used to maintain response in patients with UC after primary response to induction therapy wanes with time, resulting in secondary loss of response. METHODS: A retrospective cohort study evaluating anti-TNF-naive UC outpatients who were primary responders to adalimumab and infliximab induction therapy and who advanced onto a maintenance regimen with the respective anti-TNF agent from 2003 to 2013 was conducted. The primary outcome was the proportion of patients in each treatment group that had secondary loss of response. The secondary outcome was time to secondary loss of response, analyzed by the Kaplan-Meier method analysis. RESULTS: A total of 102 UC primary anti-TNF responders met inclusion criteria. Thirty-six patients (35.3%) were treated with adalimumab and 66 patients (64.7%) with infliximab. Mean follow-up was 139.0 weeks for adalimumab and 158.8 weeks for infliximab. A total of 21/36 (58.3%) adalimumab-treated patients and 39/66 (59.1%) infliximab-treated patients experienced a secondary loss of response during maintenance therapy. Mean time to secondary loss of response was similar for adalimumab (55.8 wk) and infliximab (59.4 wk) (P=0.82). Sex, extent of colitis, previous or concomitant azathioprine, and concurrent corticosteroids with anti-TNF induction were not associated with increased risk of secondary loss of response. CONCLUSIONS: In this real-life cohort of anti-TNF-naive primary responders with UC, the proportion of secondary loss of response and the time to secondary loss of response are similar for adalimumab and infliximab.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".