Novel Systemic Anticancer Therapy and Healthcare Utilization at the End of Life: A Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Novel systemic anticancer therapies (SACT) in the form of targeted and immunotherapies are increasingly replacing traditional chemotherapy. Little is known about the impact of novel SACT on healthcare resource utilization (HCRU) at the end of life. METHODOLOGY: A retrospective review of patients attending a tertiary cancer center in Toronto, Canada, with advanced solid or hematological malignancies who died in 2019. Demographic and cancer data, SACT use, HCRU (emergency room [ER] visits, acute/intensive care unit [ICU] admission, and place of death) were retrieved and compared between those who received SACT in their last 30 days of life and those who did not. Chi-squared tests or Quasi-Poisson regression calculated HCRU expressed as percentages or rate ratios (RR). Univariate and multivariable logistic regression identified factors independently associated with SACT use. RESULTS: Of 443 patients included, 88 (20%) received SACT in the last 30 days of life, with 42 (48%) receiving targeted therapies and 10 (11%) immunotherapy. Factors associated with SACT use included younger age (p = 0.016), breast (p < 0.001), lung (p = 0.047), hematological malignancies (p = 0.002), fewer comorbidities (p = 0.039), and novel SACT (p = 0.006). Receipt of SACT was associated with a higher frequency of ER visits (55% vs. 36% who did not receive SACT, p = 0.001), acute hospitalizations (68% vs. 47%, p < 0.001), ICU admissions (18% vs. 7%, p = 0.003), and death in hospital (45% vs. 30%, p = 0.008). CONCLUSION: Novel SACT use at the end of life is high and is strongly associated with HCRU. Future studies should explore the impact of advance care planning and palliative care referrals on SACT use.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".