Quality improvements in palliative radiotherapy at end of life: The FutRE Study.
Bibliographic record
Abstract
306 Background: Palliative radiation therapy (RT) is offered to patients with cancer for symptom management. RT planning and delivery is resource intensive. Benefits may take weeks to develop. Palliative RT at the end of life may not be completed due to patient and disease factors. RT courses that are not completed may indicate a need for improved patient selection for RT and choice of RT prescription, minimizing the likelihood of delivering futile treatment. Methods: The FUTile Radiotherapy at End of life survey was implemented in the electronic RT workspace across Alberta in 2018. Radiation oncologists (ROs) were tasked with survey completion, at the time of palliative RT prescription approval, as part of their workflow on domains pertaining to patient and treatment decision-making. This survey data was linked to the cancer registry and date of death. Data association were examined among patients completing RT within 90 days of death for the accuracy of oncologist’s provided survival prognostication estimates, number of RT fractions prescribed, number of RT fractions completed, prescribing physician, including disease factors and treatment intent. Results were explored using descriptive statistics and tests of associations (STATA 11.1) Results: 1963 RT surveys were included in our analysis. Prescribing ROs overestimated patient survival 67% of the time, by a mean of 145 days, and underestimated survival 12% of the time by a mean of 109 days. Multi-fraction RT (1403 courses) was more frequently prescribed over single fraction (SF) RT (560 courses)(one-sample t-test, p≤0.001). SF treatments were more likely to be completed than MF treatments (RR = 10.3, 95% CI = [4.85, 21.7], p < 0.0001). Treatments were less likely to be completed when survival was overestimated by 6 or more months and were over twice as likely to be completed than when patient survival was underestimated (RR = 2.6, 95% CI = [1.04, 6.50], p = 0.04). Conclusions: Survival among end of life patients is overestimated by ROs prescribing palliative MF RT 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 | 0.003 | 0.006 |
| 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 | 0.000 | 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".