Are radiation oncologists following guidelines? An audit of practice in patients with uncomplicated bone metastases
Bibliographic record
Abstract
BACKGROUND: Best-practice guidelines recommend single-fraction (SFRT) instead of multi-fraction radiation therapy (MFRT) for uncomplicated symptomatic bone metastases. SFRT is comparable to MFRT in relieving pain, convenient for patients, and cost-effective. Patterns of practice in Canada reveal that SFRT is underused, with significant variability across the country. We audited SFRT use and studied factors that may influence treatment decisions at a large academic tertiary care center in Quebec, Canada. METHODS: Patients who received radiotherapy for uncomplicated bone metastases between February 2014 and March 2015 were reviewed. Age, gender, primary histology, site of metastases and performance status were identified as potential factors affecting fractionation. These were explored by Fisher's test on univariate analysis and logistic regression for multivariate analysis. Retreatment rates were analyzed with cumulative incidence and compared with Gray's test. RESULTS: 254 radiotherapy courses were administered to 165 patients, 85.4% of which were delivered using a single fraction of 8 Gy. Patients age less than 70 years and those with breast histology were more likely to receive MFRT (p = 0.04; p = 0.0046). Performance status (ECOG) was a significant predictor of fractionation because of high correlations between young age, breast histology, and ECOG status (p = 0.03). Follow-up was too short in 40% of patients to derive definitive conclusions on retreatment. CONCLUSIONS: In accordance with current guidelines, our audit confirms that use of SFRT in patients with uncomplicated bone metastases at our center is high. We identified that patient age, primary histology, and performance status influenced fractionation. Incorporation of this quality indicator into our performance dashboard will allow assessment of retreatment differences and other criteria that may also influence treatment choice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".