Outcomes and factors influencing survival in patients with diffuse large B-cell lymphoma: a population-based analysis
Bibliographic record
Abstract
ABSTRACT Given the rapidly evolving treatment landscape for diffuse large B cell lymphoma (DLBCL), we performed a contemporary analysis of survival outcomes in patients aged ≥18 years with DLBCL at the population level using linked administrative datasets in Ontario, Canada (ICES). Among 8,675 patients (median age 67, 44% female) treated with frontline rituximab-based therapy, 1,675 (19%) were treated with second-line therapy (2L). The 2-year and 5-year overall survival (OS) from 2L were 33% and 26%, respectively. Univariate analysis demonstrated that curative-intent therapy (autologous stem cell transplant [ASCT]) (58% of patients) was associated with better OS compared to palliative radiotherapy (hazard ratio [HR] 0.56, p<0.0001). Patients ≥60 years showed inferior OS compared to those <60 (age 60-69: HR 1.35, p=0.0002; age 70-79: HR 1.64, p<0.0001; age ≥80: HR 2.08, p<0.0001). Additionally, early relapse was associated with worse outcomes compared to relapses occurring after 2 years (<3 months: HR 1.45, p=0.0002; 3-6 months: HR 1.51, p=0.0001; 6-12 months: HR 1.88, p<0.0001). Multivariable analysis confirmed these associations, while accounting for LDH, comorbidity burden, frailty, and income. Exploratory analysis indicated that third-line chimeric antigen receptor T cell therapy (CAR-T) was associated with improved outcomes compared to a historical cohort of patients treated with palliative therapy prior to 2020 (2-year OS 56% vs. 21%). This population-based analysis suggests that curative intent therapy (ASCT and CAR-T) is associated with improved OS over conventional treatment approaches. The outcomes presented here provide benchmarks for future analyses aimed at assessing the effects of novel treatments in the 2L on outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".