Linear accelerator maintenance cost analysis
Bibliographic record
Abstract
PURPOSE: Medical linear accelerators are the most costly standard equipment used in radiation oncology, however the service costs for these machines are not well understood. With an increasing demand for linear accelerators due to a global increase in cancer incidence, it is important to understand the expected maintenance costs of a larger global installed base so that these costs can be incorporated into budgeting. The purpose of this investigation is to analyze the costs for medical linear accelerator service and maintenance at our institution, in order to estimate the service cost ratio. METHODS: We collected the costs of parts used for all service work done on 32 medical linear accelerators over a two year period. The data was segregated by center, machine, linear accelerator type, and failure area in the machine. RESULTS: We found the service cost ratio (excluding software support expenses) to be 3.13% [2.74%, 3.52%,]. We observed a variability of parts costs, and overall variability of the service cost ratio to be between 2.14% and 5.25%. This result is lower than other estimates for service costs for medical equipment in general and medical linear accelerators specifically. Two-thirds of the service costs were due to labor costs, which indicate the importance of a well-trained service technician workforce. CONCLUSIONS: We estimated the service cost ratio for medical linear accelerators to be 3.13% [3.52%, 2.74%] of the initial capital cost. This result was lower than other estimates of the service cost ratio.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".