ETROLIZUMAB IMPROVED ENDOSCOPIC SCORE, PATIENT REPORTED OUTCOMES, AND INFLAMMATORY BIOMARKERS IN PATIENTS WITH MODERATE TO SEVERE UC WHO HAD FAILED TNF ANTAGONIST THERAPY: THE HICKORY OPEN-LABEL INDUCTION (OLI) COHORT
Bibliographic record
Abstract
BACKGROUND Patients with ulcerative colitis (UC) who have experienced failure of anti-TNF therapy are a difficult-to-treat population with a notable unmet medical need. The OLI Cohort of the Hickory Study (NCT02100696) evaluates etrolizumab in patients intolerant/refractory to anti-TNFs. METHODS Patients received etrolizumab s.c. 105 mg every 4 weeks in a 14-week induction period. Endoscopic subscore (ES) and patient-reported rectal bleeding (RB) and stool frequency (SF) were assessed at baseline (BL) and week 14. Assessed outcomes were clinical response, clinical remission, endoscopic improvement, RB remission and SF remission. RESULTS: HICKORY OLI enrolled 130 patients with aTNF failure. 45 % with failure of > 1 aTNF. BL scores were mean MCS of 9.4 and median fecal calprotectin (FC) of 1778 mg/kg. At week 14, SF remission was achieved in 35.4 % of patients, RB remission in 52.3 %, clinical response in 50.8 %, remission in 12.3 %, and ES ≤ 1 in 23.9 %. In the 43.9 % of patients with ≥ 1-point improvement in ES, there was an association with higher rates of SF and RB remission. Among those with ES = 0, 90 % reported SF ≤ 1, and 100 % reported RB ≤ 1. Patients achieving SF remission, RB remission, or ES ≤ 1 demonstrated > 70 % geometric mean reduction in FC. CONCLUSIONS In aTNF-failed patients with high disease burden, etrolizumab achieved clinically meaningful response, remission, and endoscopic improvement. Patients who had improved ES achieved higher rates of RB and SF remission and greater reductions in inflammatory biomarkers. Previously presented Peyrin-Biroulet L et al. UEGW 2017 Publication History Article published online: 26 May 2020 © Georg Thieme Verlag KG Stuttgart · New York
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".