Patterns of futile care for comorbidities in colorectal cancer patients near the end of life.
Bibliographic record
Abstract
e18242 Background: Futile care includes interventions that no longer provide benefit, may cause significant harm, and/or lack sufficient utility to justify the required resources. Cancer care such as chemotherapy use has been shown to be overly aggressive near the end of life. We hypothesized that futile care for non-cancer comorbidities in terminal colorectal cancer (CRC) patients may also be prevalent and associated with specific clinical factors. Methods: We constructed a large retrospective cohort of decedents aged ≥18 years who were diagnosed with CRC during 2008-2012 and who died by the end of 2013. We analyzed population-based data through linked provincial files with information on cancer management, pharmacy records, hospital discharges, and vital statistics. We defined endpoints for futile care as ≥1 hospitalization and/or ≥1 prescription for statins, dihydropyridine calcium channel blockers (CCBs), or acid suppressants within 2 months preceding death. Different time points were used in sensitivity analyses. Results: We included 2,530 patients. Median age at CRC diagnosis and death was 70 (IQR 61-79) and 72 (IQR 62-80) years, respectively. Among them, 59% were men, 66% had colon cancer, and 87% were diagnosed with advance disease. Median time from diagnosis to death was 452 (IQR 201-800) days. In terms of futile care, 4.7% had hospitalizations, 8.7% received statins, 6.8% received CCBs, and 38.2% received acid suppressants within 2 months prior to death. In multivariate analyses, there were no clear associations between demographics and hospitalizations. With respect to medication use, advanced age was correlated with increased use of statins (OR 2.235, 95%CI 1.469-3.401, p < 0.001) and CCBs (OR 2.039, 95%CI 1.304-3.190, p = 0.002), but inversely associated with use of acid suppressants (OR 0.750, 95%CI 0.598-0.941, p = 0.013). Men were also more likely to receive statins (OR 1.653, 1.099-2.488, p = 0.016), but less likely to receive acid suppressants (OR 0.764, 0.608-0.960, p = 0.021). Conclusions: Even near death, a fair number of decedents with CRC continued to receive medications for comorbidities that were unlikely to provide clinically meaningful benefits. Futile care was more prevalent in men and in the elderly.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".