Retrospective analysis of ipilimumab-induced diarrhea and/or colitis: A single centre review.
Bibliographic record
Abstract
e21064 Background: Ipilimumab is an effective medication in advanced melanoma but can cause severe diarrhea and colitis. This study identified the rate of ipilimumab-induced diarrhea/colitis at the Juravinski Cancer Centre (JCC), its associated factors for development, overall survival (OS) and progression free survival (PFS). Methods: The Ontario Patient Information System was used to retrospectively identify all melanoma patients at the JCC who were treated with ipilimumab 3 mg/kg IV every 3 weeks (September 2012 to June 2016). Patient demographics, medical history, prior melanoma treatments, diagnosis of ipilimumab-induced diarrhea/colitis, interventions to treat the diarrhea/colitis, and OS and PFS were collected. Descriptive statistics summarized characteristics and outcomes. Kaplan-Meier methods estimated time to event outcomes. Cox regression evaluated whether markers were prognostic for time to diarrhea/colitis diagnosis. Results: 71 patients were treated with ipilimumab at the JCC, of which 22 patients (31%) developed diarrhea/colitis of any Grade; 4 patients developed Grade 1, 5 patients Grade 2, 6 patients Grade 3, 3 patients Grade 4, and 4 patients had unclear Grade. 11 patients required prednisone 1-2 mg/kg and 2 patients required anti-TNF treatment to treat their diarrhea/colitis; 1 patient required colectomy due to perforation. 10 patients required treatment discontinuation due to diarrhea/colitis. Whole cohort median OS and PFS was 340 days (95% CI 205, 519) and 110 days (95% CI 91, 138), respectively. Univariate analysis showed that only inadequate hematologic function at time of first ipilimumab application was prognostic of diarrhea/colitis (HR = 6.42, 95% CI 1.44, 28.62; p = 0.015). Conclusions: Our OS, PFS, and rate of all grade ipilimumab-induced diarrhea/colitis are similar to published data, however our rate of Grade > 2 is larger. Additional work is needed to identify risk factors for the development of this immune related adverse event.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".