PTH-072 Corticosteroid Dose Reduction in Ulcerative Colitis Patients Treated with Vedolizumab
Bibliographic record
Abstract
Introduction Corticosteroids (CS) are effective for the short-term treatment of patients (pts) with ulcerative colitis (UC), but serious side effects prohibit long-term use. In the GEMINI 1 study, a higher percentage of pts with moderately to severely active UC were in CS-free remission at week (wk) 52 with vedolizumab (VDZ) treatment than with placebo (PBO).1 Methods In GEMINI 1, pts who responded to VDZ induction therapy at wk 6 were re-randomised to PBO or VDZ for 46 wks. From wk 6 onward, pts with clinical response discontinued CS use. We characterised CS dose reductions achieved with VDZ therapy in exploratory and post hoc analyses of pts with baseline (wk 0) CS use (≤30 mg/day prednisone or equivalent). Median CS dose over time, change from baseline CS dose, and CS-free status at wk 52 were summarised overall and by anti-tumour necrosis factor (anti-TNF) treatment (naïve or failure) history. Results Of pts with baseline CS use, 74% decreased their CS dose with VDZ treatment at week 52 (vs 57% with PBO) (Table). At wk 52, 56% of VDZ-treated pts were on ≤7.5 mg/day of CS (Table), and the median CS dose was 2.5 mg/day for VDZ-treated pts and 10.0 mg/day for PBO. Numerically higher percentages of VDZ-treated pts were CS-free for 90 and 180 consecutive days at wk 52 than PBO- treated pts. Similar trends were observed in the anti-TNF-naïve and anti-TNF-failure populations. Conclusion Numerically greater reductions in CS use were achieved with VDZ maintenance therapy compared with PBO. At week 52, VDZ therapy was associated with numerically higher percentages of CS-free patients and patients who were CS-free for 90 or 180 consecutive days than PBO. Interpretation of these post hoc analyses, including the degree of dose reduction, is limited by differing initiation weeks for CS tapering per patient and small sample sizes. Reference 1 Feagan BG, et al. N Engl J Med. 2013;369:699–710; NCT00783718. Disclosure of Interest E. Loftus Jr Grant/research support from: from AbbVie, Janssen, UCB, Takeda, Pfizer, GlaxoSmithKline, Amgen, Bristol-Myers Squibb, Genentech, Robarts Clinical Trials, Gilead, Receptos, Consultant for: AbbVie, Janssen, UCB, Takeda, Immune Pharmaceuticals, Celgene, MedImmune, Theradiag, Genentech Inc, Seres Health, Sun Pharmaceuticals, Bristol-Myers Squibb, C. Siegel Grant/research support from: AbbVie, Janssen, Salix, Takeda, UCB, Consultant for: AbbVie, Amgen, Janssen, Lilly, Pfizer, Takeda, UCB, Speaker bureau with: AbbVie, Janssen, Takeda, R. Panaccione Grant/research support from: Abbott, and UCB Inc, Consultant for: Abbott, Biogen/IDEC, Axcan Pharma Inc, Bristol-Myers Squibb, Centocor, Inc, Chemocentryx, Ferring Pharmaceuticals Inc, Genentech Inc, Lippincott Williams & Wilkins, Medscape, Osiris Therapeutics, Inc, Novartis Pharmaceuticals, Genentech Inc, Elan Pharmaceuticals, Inc, UCB, Inc, W. Sandborn Grant/research support from: Janssen, AbbVie, Pfizer, Amgen, Genentech, Consultant for: Janssen, AbbVie, Pfizer, Amgen, Genentech, Takeda, Speaker bureau with: AbbVie, Takeda, M. Smyth Employee of: Takeda Development Centre Europe Ltd, London, UK, A. James Employee of: Takeda Development Centre Europe Ltd, London, UK, J. Xu Employee of: Takeda Pharmaceuticals International Co, Cambridge, Massachusetts, USA, B. Abhyankar Employee of: Takeda Development Centre Europe Ltd, London, UK
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".