AN ECONOMIC FRAMEWORK FOR EVALUATING A MULTILEAF COLLIMATOR
Bibliographic record
Abstract
OBJECTIVES: As health care budgets continue to face close scrutiny, any new acquisition must be evaluated for both costs and outcomes. This study was undertaken to demonstrate the application of an economic framework for the evaluation of a multileaf collimator as an example of a new technology that can have both quantifiable and nonquantifiable benefits for patients, staff, and cancer care institutions. METHODS: Using financial data from the Northeastern Ontario Regional Cancer Centre (NEORCC) and a recognized staffing model, a commercial spreadsheet, developed to economically characterize the principal radiotherapy processes has been used to determine the net incremental annual cost of a multileaf collimator (MLC). RESULTS: The incremental annual cost of purchasing an MLC is estimated at approximately $85,000 (1997 CDN $). Without increasing patient throughput, this increases the average cost of a course of radiotherapy by approximately CDN $200. Savings can be accrued by decreasing mold room activity, increasing the hourly patient capacity on each treatment machine, and decreasing sick time due to strain injuries. CONCLUSIONS: Although the clinical outcome of techniques facilitated by MLCs, such as intensity-modulated radiation therapy, are unknown at this time, an economic context within which to objectively evaluate this technology is presented. The framework presented suggests a method of quantifying outcome-justified expenditures, such as improved patient outcome and greater treatment flexibility, which, when offset against the incremental annual equipment cost, may be used to help justify the acquisition of multileaf technology.
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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 | 0.020 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".