Health care utilization and costs for frail vs nonfrail patients with diffuse large B-cell lymphoma
Bibliographic record
Abstract
ABSTRACT: Half of older patients with diffuse large B-cell lymphoma (DLBCL) receiving curative-intent treatment are frail. Understanding the differences in health care utilization including costs between frail and nonfrail patients can inform appropriate models of care. A retrospective cohort study was conducted using population-based data in Ontario, Canada. Patients aged ≥66 years with DLBCL who received frontline curative-intent chemoimmunotherapy between 2006 and 2017 were included. Frailty was defined using a cumulative deficit-based frailty index. Health care utilization and costs were grouped into 5 phases: (1) 90 days preceding first treatment; (2) early treatment (0 to +90 days after starting treatment); (3) late treatment (+91 to +180 days); (4) follow-up (+181 to -181 days before death); and (5) end of life (last 180 days before death). Costs were standardized to 30-day intervals (2019 Canadian dollars). A total of 5527 patients were included (median age, 75 years; 48% female). A total of 2699 patients (49%) were classified as frail. The median costs for frail vs nonfrail patients per 30 days based on phase of care were (1) $5683 vs $2586 ; (2) $13 090 vs $11 256; (3) $5734 vs $4883; (4) $1138 vs $686; and (5) $11 413 vs $9089; statistically significant in all phases. In multivariable modeling, frail patients had higher rates of emergency department visits and hospitalizations and increased costs than nonfrail patients through all phases except end-of-life phase. During end-of-life phase, a substantial portion of patients (n = 2569 [84%]) required admission to hospital; 684 (27%) required intensive care unit admission. Future work could assess whether certain hospitalizations are preventable, particularly for patients identified as frail.
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) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".