End-of-life interventions in patients with cancer
Bibliographic record
Abstract
OBJECTIVES: To describe variations in the receipt of potentially inappropriate interventions in the last 100 days of life of patients with cancer according to patient characteristics and cancer site. METHODS: We conducted a population-based retrospective cohort study of cancer decedents in Ontario, Canada who died between 1 January 2013 and 31 December 2018. Potentially inappropriate interventions, including chemotherapy, major surgery, intensive care unit admission, cardiopulmonary resuscitation, defibrillation, dialysis, percutaneous coronary intervention, mechanical ventilation, feeding tube placement, blood transfusion and bronchoscopy, were captured via hospital discharge records. We used Poisson regression to examine associations between interventions and decedent age, sex, rurality, income and cancer site. RESULTS: Among 151 618 decedents, 81.3% received at least one intervention, and 21.4% received 3+ different interventions. Older patients (age 95-105 years vs 19-44 years, rate ratio (RR) 0.36, 95% CI 0.34 to 0.38) and women (RR 0.94, 95% CI 0.93 to 0.94) had lower intervention rates. Rural patients (RR 1.09, 95% CI 1.08 to 1.10), individuals in the highest area-level income quintile (vs lowest income quintile RR 1.02, 95% CI 1.01 to 1.04), and patients with pancreatic cancer (vs colorectal cancer RR 1.10, 95% CI 1.07 to 1.12) had higher intervention rates. CONCLUSIONS: Potentially inappropriate interventions were common in the last 100 days of life of cancer decedents. Variations in interventions may reflect differences in prognostic awareness, healthcare access, and care preferences and quality. Earlier identification of patients' palliative care needs and involvement of palliative care specialists may help reduce the use of these interventions at the end of life.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".