Anti-TNF Therapy Within 2 Years of Crohnʼs Disease Diagnosis Improves Patient Outcomes
Bibliographic record
Abstract
BACKGROUND: Although biological agents targeting tumor necrosis factor (TNF) alpha are effective in the management of Crohn's disease (CD), use of anti-TNF agents is often delayed until after failure of other treatment modalities, resulting in potentially long delays between diagnosis and initiation of infliximab or adalimumab. We aim to determine if early treatment with anti-TNF agents reduces the rate of surgical resection and clinical secondary loss of response in CD patients. METHODS: A retrospective cohort study was conducted evaluating CD outpatients who were primary responders to anti-TNF therapy, on a maintenance regimen with infliximab or adalimumab from 2003 to 2014. Patients were stratified by time to first dose of anti-TNF therapy; early initiation was defined as starting anti-TNF therapy within 2 years of diagnosis. The primary outcome was occurrence of surgical resection or clinical secondary loss of response requiring dose escalation. Kaplan-Meier analysis was used to assess time to the primary outcomes. RESULTS: One hundred ninety CD patients met inclusion criteria (100 infliximab, 90 adalimumab). Median follow-up duration was 154.4 weeks (inter quartile range, 106.4-227.8). Fifty-three patients (27.9%) had early initiation of anti-TNF therapy. Fewer patients in the early initiation group required surgery (5.7% versus 30.7%, P < 0.001) or experienced clinical secondary loss of response (45.3% versus 67.2%, P = 0.006). In Kaplan-Meier analysis, early initiation of anti-TNF therapy prolonged time to surgery (P = 0.001) and secondary loss of response (P = 0.006). CONCLUSIONS: In CD patients, early initiation of infliximab or adalimumab within the first 2 years of diagnosis reduces the rate of surgery and secondary loss of response requiring dose escalation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".