End-of-Life Cancer Care in Ontario and the United States: Quality by Accident or Quality by Design?
Bibliographic record
Abstract
In this month’s issue of the Journal, Warren et al. ( 1 ) report the results of their carefully conducted analysis of patients who died with non–small cell lung cancer in the United States and Ontario. The study design selected a group of patients with well-documented advanced cancer at diagnosis. Given the cancer type and stage, the clinical teams caring for these patients would have known the generally poor survival prognosis. The article reports two major findings: care patterns differed between the United States and Ontario and the overall use of community palliative care appears to have fallen short of the average patient preferences in both countries—most patients prefer supportive measures that avoid a hospital death ( 2 , 3 ). The US patients received more chemotherapy, whereas Ontario patients had more inpatient days, greater emergency room use, and were much more likely to die in the hospital. Despite relatively high use of community supportive care, the rates of inpatient death are too high in the United States and much too high in Ontario.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".