Novel Systemic Anticancer Treatments and Health Services Use at the End of Life Among Adults With Cancer
Bibliographic record
Abstract
PURPOSE: Use of chemotherapy at the end of life (EOL) is discouraged, but evidence to guide decisions on the use of novel systemic anticancer treatment (SACT) agents is lacking. We examined trends of use among SACT types and association with health services use at the EOL. MATERIALS AND METHODS: We analyzed Canadian Ontario Cancer Registry data for adults diagnosed with solid tumors or hematologic malignancies within 5 years of death who received SACT between March 2015 and March 2021. Receipt of SACT in the last 30 days of life was categorized as chemotherapy alone, chemotherapy and immunotherapy, immunotherapy alone, and targeted therapy alone. Outcomes included high health services use, including multiple (≥2) emergency department (ED) visits, multiple (≥2) hospitalizations, or any (≥1) intensive care unit admission, and hospital deaths. Segmented linear regression estimated monthly trends; multivariable logistic regression estimated adjusted odds ratios (aORs) of outcomes for various SACT types. RESULTS: < .001). Adjusted odds of high health services use and hospital death were more than two-fold greater among patients receiving SACT at the EOL (vs. none); individual aORs of high health services use and hospital death were 2.20 and 2.72 for chemotherapy alone, 2.36 and 3.10 for chemotherapy and immunotherapy, 1.92 and 2.27 for immunotherapy alone, and 1.75 and 2.37 for targeted therapy alone, respectively. CONCLUSION: Use of SACT at the EOL increased significantly over time, driven by increased use of immunotherapy. SACT use at the EOL, regardless of its type, was associated with high health services use and hospital death. Guidelines on the use of SACT at the EOL should include novel cancer treatments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".