Time Spent in Hospital in the Last Six Months of Life in Patients Who Died of Cancer in Ontario
Bibliographic record
Abstract
PURPOSE: To describe hospital bed utilization in the final 6 months of life in patients dying of cancer in Ontario, Canada. PATIENTS AND METHODS: Hospital separation records were linked to a population-based cancer registry to identify factors associated with hospitalization in the 203,713 patients who died of cancer in Ontario between 1986 and 1998. RESULTS: Between 1986 and 1998, 5.3% of all acute care beds in Ontario were devoted to the care of cancer patients in the last 6 months of life. The mean time spent in hospital in the last 6 months of life decreased from 34.3 days in 1986 to 22.7 days in 1998. Hospitalization rates increased exponentially during the last month of life. Patients younger than 50 years of age, women, and residents of poorer communities spent significantly longer in hospital than others. Hospitalization rates differed very little among the common solid tumors, but patients with CNS malignancies, the lymphomas, and the leukemias spent significantly longer in hospital than the other groups. There was significant interregional variations in hospitalization that were not explained by differences in case mix. There was a statistically significant inverse correlation between the rate of use of palliative radiotherapy and the hospital bed use in the county in which the patient resided. CONCLUSION: The total time spent in hospital in the last 6 months of life has decreased over the last decade, but acute care hospitals continue to play a large role in the care of patients who are dying of cancer.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".