Quality of <scp>end‐of‐life</scp> care in multiple myeloma: A <scp>13‐year</scp> analysis of a population‐based cohort in Ontario, Canada
Bibliographic record
Abstract
Optimizing end-of-life (EOL) care for multiple myeloma (MM) represents an unmet need. An administrative cohort in Ontario, Canada was analysed between 2006 and 2018. Aggressive care was defined as two or more emergency-department visits in the last 30 days before death, or at least two new hospitalizations within 30 days of death, or an intensive care unit (ICU) admission within the last 30 days of life. Supportive care was defined as a physician house-call in the last two weeks before death, or a palliative nursing or personal support visit at home in the last 30 days before death. Among 5095 patients, 23.2% of patients received chemotherapy at EOL and 55.6% of patients died as inpatient. A minority received aggressive care at EOL [28.3%: autologous stem cell transplant (ASCT), 20.4%: non-ASCT], and a majority received supportive care at EOL (65.4%: ASCT, 61.5%: non-ASCT). Supportive care was less likely to be received by those aged over 80 years and in lower-income neighbourhoods. Supportive care at EOL increased from 56.0% in 2006 to 70.3% in 2018. Despite improvements, many patients with MM experience aggressive care at EOL. Even in a publicly funded health care system, disparities based on age, income and community size are present.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".