Treatment patterns and outcomes among mantle cell lymphoma patients treated with ibrutinib in the United States: a retrospective electronic medical record database and chart review study
Bibliographic record
Abstract
Summary The experience of patients with mantle cell lymphoma (MCL) in community oncology practices, including reasons for treatment discontinuation, is sparse. This retrospective study sought to elucidate treatment patterns and outcomes of patients with MCL treated with ibrutinib in the community setting. Patients were identified from the US Oncology Network electronic medical records database, iKnowMed TM , between 1 November 2013 and 31 October 2016. Descriptive analysis was performed to describe the demographic and clinical characteristics of the population. Kaplan–Meier estimates were performed to determine clinical outcomes. A Cox proportional hazards model was used to identify predictors of survival. Of the 1914 patients identified with MCL, 159 were treated with ibrutinib. The median age was 71 years and the majority were male (76%) and Caucasian (89%). The overall discontinuation rate was 83·6%; the most common reasons were progression (35%) and toxicities (25·6%). The median overall survival and progression‐free survival was 25·82 months (95% confidence interval [CI] 19·94, NR) and 19·55 months (95% CI 16·52, 24·28) respectively. In multivariate modelling, patient age was predictive of survival (hazard ratio 1·041, P = 0·0186). Ibrutinib was temporarily reduced in 16·4% ( n = 26) and held in 30·2% ( n = 48), primarily due to toxicity 66·7% ( n = 32). Survival data showed similarities between community oncology practices and clinical trials.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".