Quality matters: Patterns of palliative interventions in metastatic colorectal cancer (mCRC) patients.
Bibliographic record
Abstract
83 Background: Increasing recognition that high-quality end of life care is essential has resulted in internationally endorsed metrics allowing assessment of interventions at the end of life. Median survival for mCRC patients has improved to more than 24 months resulting in increased opportunity to undergo interventions for symptom relief at the end of life. We explored patterns of palliative interventions (chemotherapy, radiotherapy, surgery, endoscopy, drainage procedures) and outcomes in mCRC patients. Methods: A retrospective review was undertaken of all mCRC patients referred to the palliative care service from 2000 to 2010 at a tertiary cancer center in Toronto, Canada. Descriptive statistics, survival analysis and regression were employed. Results: A total of 542 patients were included of whom 52.8% were male, mean age was 62.8 years and 44.6% had stage 4 disease at diagnosis. Over the course of their disease 93.9% had an intervention at any time after their diagnosis including 27.5% of patients undergoing palliative surgery, 77% of patients had an intervention in the last year of life and 19.1% had an intervention in the last 30 days of life. The percentage of patients receiving interventions within the last 14 days of life were 1.23% for chemotherapy, 4.6% for radiotherapy, 0.5% for surgery, 10.4% for endoscopy and 23% drainage procedures. The mean time between referral to palliative care and death was 7 months (SD 10.4). For patients who received chemotherapy, the mean time between last chemotherapy and death was 9.5 months (SD 14.9). Overall survival for patients who did not receive chemotherapy was 28 months (SD 33) compared with 40 months (SD 32) for those who received chemotherapy. Regression analysis for risks of dying within 30 days of chemotherapy was limited by a low event rate. Increasing age was significantly associated with a lower risk of dying within 30 days of chemotherapy. Conclusions: In their final months of life, palliative mCRC patients undergo a significant number of interventions aiming to improve quality of life. These require considerable multi-disciplinary input with ramifications for quality care, planning for service provision and funding.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".