Improving radiotherapy quality of care: Examining practice patterns in regional centers.
Bibliographic record
Abstract
53 Background: Radiotherapy (RT) is a resource intensive cancer treatment. Benefits may take weeks to realize, whereas side effects are more immediate. RT is commonly delivered with palliative intent often near the end of life (EOL). In patients with limited life expectancy, single fractions (sf) may substitute for multi-fraction (mf) treatments. Objectives were to document RT courses delivered to patients with cancer at EOL, with the goal of informing treatment practices, developing a clinical decision-making tool and reducing potentially futile treatment and side effects for patients. Methods: Cancer Registry data was examined with ethics approval. Patient and prospective treatment data were collected and analyzed to determine the number of RT courses starting within 90 days of death (EOL) among those with cancer diagnoses who died between January 1, 2016, and July 31, 2017. Data was stratified by demographics, disease factors, RT center and prescription factors. Results are reported in aggregate and explored using tests of association (Stata 11.1). Results: Among 1874 patients who died, 2448 RT courses were identified across 4 RT centers. Of those prescribed RT at the EOL, there were 442 sf courses 100% completed, 1267 2-5 fraction courses 90% completed, 552 6-10 fraction courses 77% completed, and 187 11+ fraction courses 57% completed. Among patients prescribed RT 14 days prior to death, 24% had been prescribed a sf of RT compared to 18% of those within 90 days of death (Chi2 17.92, p < 0.001). Among patients prescribed RT 90 days prior to death, 25% of patients in tertiary Centre A were prescribed a sf compared to 13% of patients in tertiary Centre B (Chi2 121.90, p < 0.001). Patients were half as likely (RR = 0.47, 95% CI [0.39, 0.56], p < 0.001) to complete a mf treatment within 14 days of their death as compared to within 90 days. Patients who had a diagnosis to death interval of under a year were less likely to complete RT than those over a year (RR = 0.82, 95% CI [0.79, 0.85], p < 0.001). Conclusions: Multi-fraction RT is often not completed prior to death. Treating RT center is associated with differences in prescribing patterns. Better understanding of factors influencing RT practice patterns may lessen the proportion of potentially futile RT.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".