Could changes in clinical practice reduce waiting lists for radiotherapy?
Bibliographic record
Abstract
OBJECTIVES: To describe variation in the practice of radiation oncology among the nine cancer centres in Ontario, and to explore the impact of variations in the number of treatments prescribed per case on the overall demand for radiotherapy in the province. METHODS: Prospectively collected, electronic records of all courses of radiotherapy given at the nine radiotherapy centres in Ontario between 1 January 1996 and 31 December 1997 were analysed to describe inter-centre variations in the number of radiation treatments (fractions) prescribed per case. The effect of the observed inter-centre variations in practice on the total provincial workload was modelled. RESULTS: During the two-year study, 672,292 fractions were administered as 44,096 courses of treatment. On average there were 63.6 high-energy treatment machines operating in the province over this period. The mean number of fractions per course for the province as a whole was 15.3, and ranged from 10.9 at centre D to 16.0 at centre F. The inter-centre variation in the number of fractions per course was higher when radiotherapy was used palliatively than curatively. The range of variation in the number of treatments per curative course was disease-specific; it was highest for breast cancer, and lowest for the gynaecological malignancies. If each clinical problem had been treated everywhere in the province as it was at the centre which treated it with the fewest treatments, 77,274 fewer radiation treatments each year would have been required to treat the same number of cases. This is equivalent to the capacity of 14.6 treatment machines, and exceeds the estimated shortfall in the current supply of radiotherapy in the province. Province-wide adoption of more parsimonious approaches to the use of palliative radiotherapy, or to the use of curative (adjuvant) radiotherapy in breast cancer, would produce dramatic decreases in the overall demand for radiotherapy machine time. In these particular situations, the literature indicates that shorter courses of treatment are just as effective, and as well tolerated, as longer courses. CONCLUSIONS: Variation in the way that oncologists prescribe radiotherapy is an important determinant of overall radiotherapy machine workload. Modest changes in prescribing by radiation oncologists, even within the range of current practice in Ontario, and within the range of fractionation schemes that have been shown to produce optimal results, have the potential to reduce waiting lists for radiotherapy.
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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 | 0.007 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".