Frailty in Patients With Newly Diagnosed Diffuse Large B-Cell Lymphoma Receiving Curative-Intent Therapy: A Population-Based Study
Bibliographic record
Abstract
BACKGROUND: The objectives of this study were to determine whether frailty is associated with survival in a population-based sample of patients with diffuse large B-cell lymphoma (DLBCL) and to describe the healthcare utilization patterns of frail versus nonfrail patients during treatment. METHODS: A retrospective cohort study was conducted using population-based data in Ontario, Canada. Patients aged ≥66 years diagnosed between 2006 and 2017 with DLBCL or transformed follicular lymphoma who received first-line curative-intent chemoimmunotherapy were included. Frailty was defined using a modified version of a generalizable frailty index developed for use with Ontario administrative data. Cox regression was performed to examine the association between frailty and 1-year mortality. RESULTS: A total of 5,527 patients were included (median age, 75 years [interquartile range, 70-80 years]; 48% female), of whom 2,699 (49%) were classified as frail. Within 1 year of first-line treatment, 32% (n=868) of frail patients had died compared with 20% (n=553) of nonfrail patients (unadjusted hazard ratio, 1.8; 95% CI, 1.6-2.0; P<.0001). Frail patients had higher healthcare utilization during treatment, with most hospitalizations related to infection and/or lymphoma. In multivariable modeling controlling for age, inpatient diagnosis, number of chemoimmunotherapy cycles received, comorbidity burden, and healthcare utilization, frailty remained independently associated with 1-year mortality (adjusted hazard ratio, 1.5; 95% CI, 1.3-1.7; P<.0001). CONCLUSIONS: In a population-based sample of older adult patients with DLBCL receiving front-line curative-intent therapy, half were classified as frail, and their adjusted relative rate of death in the first year after starting treatment was 50% higher than that of nonfrail patients. Frailty seems to be associated with poor treatment tolerance and a higher likelihood of requiring acute hospital-based care.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".