Technology Resource Planning in Radiation Oncology: Application of a Needs-Based Analytic Framework to Radiosurgery Planning in Ontario
Bibliographic record
Abstract
PURPOSE: With the emergence of radiosurgery as a new radiotherapeutic technique, health care decision makers are required to allocate capital radiotherapy resources to meet both current and future radiosurgery requirements. The goal of this article is to demonstrate the feasibility of applying an explicit, needs-based model to resource planning in radiation oncology. METHODS: Using an analytic model that relates radiosurgery need to population size, epidemiology, level of service planned, and productivity, the current radiosurgical need for single brain metastases in Ontario was estimated. The model was populated using Ontario-specific data where possible and supplemented with information from the published literature. Multiway sensitivity analyses were performed to calculate the minimum and maximum technology requirements. RESULTS: The calculated number of full-time radiosurgical units required to treat patients with single brain metastases in Ontario was 5.9. Sensitivity analyses performed varying both level of service planned and productivity yielded a range of requirements from 2.5 to 12.2 full-time radiosurgery units. CONCLUSION: We have shown through the example of single brain metastases in Ontario that it is feasible to perform explicit, needs-based resource planning in radiation oncology. As the availability of new specialized technology increases, health care decision makers may use this approach to ensure the needs of their population are met while maximizing productivity and minimizing opportunity cost.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".