Quality of end-of-life care for patients with multiple myeloma: A 12-year analysis of a population-based cohort.
Bibliographic record
Abstract
12031 Background: Despite treatment advances, multiple myeloma (MM) remains a significant source of morbidity and mortality. The end of life for patients with MM has not previously been examined within the context of a population-based cohort in a publicly funded health system. Methods: We retrospectively analyzed patients with death attributable to MM between 2006-2018 using ICES linked databases in the public health care system in Ontario, Canada. Aggressive care was defined as two or more emergency department visits in the last 30 days before death, at least two new hospitalizations within 30 days of death, or an ICU admission within 30 days of death. Supportive care was defined as physician house call 2 weeks before death, or a palliative nursing or personal support visit at home in last 30 days before death. Multivariable logistic regression models were used to assess for factors predisposing to aggressive or supportive care. Patients were stratified based on receipt of autologous stem cell transplant (ASCT). Results: In total, 5095 patients were included (Table). Overall, 23.2% of patients received chemotherapy in last two weeks of life and 55.6% of patients died in the hospital. Most patients were admitted to hospital within the last 30 days of life (73.4%:ASCT cohort, 61.4%:non-ASCT cohort). A minority received aggressive care at end of life (28.3%:ASCT cohort, 20.4%:non-ASCT cohort), and a majority received supportive care at end of life (65.4%:ASCT cohort, 61.5%:non-ASCT cohort). Multivariate regression models showed that patients ≥ 80 years (compared to 60-69) were less likely to receive aggressive care (OR=0.54, 95% CI=0.42-0.68), and those with residence in smaller size community of < 10,000 were more likely to receive aggressive care (OR=1.89, 95% CI=1.5-2.4). Supportive care was significantly less likely to be received by patients (OR=0.72, 95% CI= 0.59 to 0.88) and more likely to be received by patients aged 18-49 (OR=1.9, 95% CI=1.2-3.1). Neighbourhoods with lowest income quintiles (OR=0.65, 95% CI=0.53-0.78) were less likely to receive supportive care. When trended over time, patients receiving supportive care at end of life increased (56.0% in 2006 to 70.3% in 2018). Conclusions: We demonstrate that despite improvements over time, a substantial number of patients with MM experience aggressive care and hospitalizations at the end of life. Despite this being a publicly funded system, disparities in end-of-life care based on age, income and area of residence are present.[Table: see text]
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".