Patterns of Practice in the Prescription of Palliative Radiotherapy for the Treatment of Bone Metastases at the Rapid Response Radiotherapy Program between 2005 and 2012
Bibliographic record
Abstract
OBJECTIVE: We examined whether patterns of practice in the prescription of palliative radiation therapy for bone metastases had changed over time in the Rapid Response Radiotherapy Program (rrrp). METHODS: After reviewing data from August 1, 2005, to April 30, 2012, we analyzed patient demographics, diseases, organizational factors, and possible reasons for the prescription of various radiotherapy fractionation schedules. The chi-square test was used to detect differences in proportions between unordered categorical variables. Univariate logistic regression analysis and the simple Fisher exact test were also used to determine the factors most significant to choice of dose-fractionation schedule. RESULTS: During the study period, 2549 courses of radiation therapy were prescribed. In 65% of cases, a single fraction of radiation therapy was prescribed, and in 35% of cases, multiple fractions were prescribed. A single fraction of radiation therapy was more frequently prescribed when patients were older, had a prior history of radiation, or had a prostate primary, and when the radiation oncologist had qualified before 1990. CONCLUSIONS: For patients with bone metastasis, a single fraction of radiation therapy was prescribed with significantly greater frequency.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".