Do Sustainable Palliative Single Fraction Radiotherapy Practices Proliferate or Perish 2 Years after a Knowledge Translation Campaign?
Bibliographic record
Abstract
In early 2017, the Canadian Partnership Against Cancer and CancerCare Manitoba undertook a comprehensive knowledge translation (KT) campaign to improve the utilization of single fraction radiotherapy (SFRT) over multiple fraction radiotherapy (MFRT) for palliative management of bone metastases. The campaign significantly increased short-term SFRT utilization. We assess the time-dependent effects of KT-derived SFRT utilization 12-24 months removed from the KT campaign in a Provincial Cancer Program. This study identified patients receiving palliative radiotherapy for bone metastases in Manitoba in the 2018 calendar year using the provincial radiotherapy database. The proportion of patients treated with SFRT in 2018 was compared to 2017. Logistic regression analyses identified risk factors associated with MFRT receipt. In 2018, 1008 patients received palliative radiotherapy for bone metastasis, of which 63.3% received SFRT, a small overall increase in SFRT use over 2017 (59.1%). However, 41.1% of ROs demonstrated year-over-year decreases in SFRT utilization, indicative of a time-dependent loss of SFRT prescription habits derived from KT. Although SFRT use increased slightly overall in 2018, evidence of compliance fatigue was observed, suggestive of a time-perishing property of RO prescription behaviours derived from KT methodologies. Verification of the study's findings in larger cohorts would be beneficial. These findings highlight the need for additional longitudinal KT reinforcement practices in the years following KT campaigns.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".