Impact of aggressive management and palliative care on cancer costs in the final month of life
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: A significant share of the cost of cancer care is concentrated in the end-of-life period. Although quality measures of aggressive treatment may guide optimal care during this timeframe, little is known about whether these metrics affect costs of care. METHODS: This study used population data to identify a cohort of patients who died of cancer in Ontario, Canada (2005-2009). Individuals were categorized as having received or having not received aggressive end-of-life care according to quality measures related to acute institutional care or chemotherapy administration in the end-of-life period. Costs (2009 Canadian dollars) were collected over the last month of life through the linkage of health system administrative databases. Multivariate quantile regression was used to identify predictors of increased costs. RESULTS: Among 107,253 patients, the mean per-patient cost over the final month was $18,131 for patients receiving aggressive care and $12,678 for patients receiving nonaggressive care (P < .0001). Patients who received chemotherapy in the last 2 weeks of life also sustained higher costs than those who did not (P < .0001). For individuals receiving end-of-life care in the highest cost quintile, early and repeated palliative care consultation was associated with reduced mean per-patient costs. In a multivariate analysis, chemotherapy in the 2 weeks of life remained predictive of increased costs (median increase, $536; P < .0001), whereas access to palliation remained predictive for lower costs (median decrease, $418; P < .0001). CONCLUSIONS: Cancer patients who receive aggressive end-of-life care incur 43% higher costs than those managed nonaggressively. Palliative consultation may partially offset these costs and offer resultant savings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 it