ORIGINAL ARTICLE Variations in intensity of end-of-life cancer therapy by cancer type at a Canadian tertiary cancer centre between 2003 and 2010
Bibliographic record
Abstract
Background Aggressive medical management of cancer pa-tients at the end of life (EOL) is an indicator of health services quality. We evaluated the variations in EOL cancer therapy uti-lization and in acute care hospital deaths across different types of cancer within the setting of a regionalized cancer program. Methods Intravenous chemotherapy and radiotherapy use within the last 14 and 30 days of life was identified through the Alberta Cancer Registry and then verified by chart review for cancer decedents residing within 50 km of the Tom Baker Cancer Centre between 2003 and 2010. Multivariable logistic regression was used to examine variations in outcomes of interest by cancer, adjusting for age and other factors in prespecified models. Results Of the 9863 decedents included in the study, 3.0 and 6.3 % received chemotherapy within the final 14 and 30 days of life, respectively. In multivariable model, breast, hemato-logical, and gynecological cancers were at least 2.5 times more likely than other cancers to undergo EOL chemotherapy. Radiotherapy was given to 4.6 % of decedents within 14 days of death, but only 66 % (359/542 courses) were completed as prescribed. Acute care admission within 14 days of death was seen in 44 % of decedents and 34 % died in the hospital. Conclusions In our regional cancer program, the intensity of cancer therapies near the end of life varied considerably across different cancer types. Such variations may be unwarranted. A substantial proportion of cancer deaths occurred in the acute care setting. Greater efforts to integrate palliative care in out-patient cancer services are needed.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".