Similar Clinical and Surgical Outcomes Achieved with Early Compared to Late Anti-TNF Induction in Mild-to-Moderate Ulcerative Colitis: A Retrospective Cohort Study
Bibliographic record
Abstract
Background. Biologic agents targeting tumor necrosis factor alpha are effective in the management of ulcerative colitis (UC), but their use is often postponed until after failure of other treatment modalities. Objectives. We aim to determine if earlier treatment with infliximab or adalimumab alters clinical and surgical outcomes in UC patients. Methods. A retrospective cohort study was conducted evaluating UC outpatients treated with infliximab or adalimumab from 2003 to 2014. Patients were stratified by time to first anti-TNF exposure; early initiation was defined as starting treatment within three years of diagnosis. Primary outcomes were colectomy, UC-related hospitalization, and clinical secondary loss of response. Kaplan-Meier analysis was used to assess time to the primary outcomes. Results. 115 patients were included (78 infliximab, 37 adalimumab). Median follow-up was 175.6 weeks (IQR 72.4-228.4 weeks). Fifty-seven (49.6%) patients received early anti-TNF therapy; median time to treatment in this group was 38.1 (23.3-91.0) weeks compared to 414.0 (254.0-561.3) weeks in the late initiator cohort (p < 0.0001). Patients treated with early anti-TNF therapy had more severe endoscopic disease at induction (mean Mayo endoscopy subscore 2.46 (SD ± 0.66) versus 1.86 (±0.67), p < 0.001) and trended towards increased risk of colectomy (17.5% versus 8.6%, p = 0.16) and UC-related hospitalization (43.9% versus 27.6%, p = 0.07). In multivariate regression analysis, early anti-TNF induction was not associated with colectomy (HR 2.02 [95% CI: 0.57-7.20]), hospitalization (HR 1.66 [0.84-3.30]), or secondary loss of response (HR 0.86 [0.52-1.42]). Conclusions. Anti-TNF therapy is initiated earlier in patients with severe UC but earlier treatment does not prevent hospitalization, colectomy, or secondary loss of response.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".