TU-C-BRB-07: Medical Physics Staffing for Radiation Treatment: A Robust Algorithm with Trans-Canada Validation
Bibliographic record
Abstract
Purpose: To describe an algorithm for determining appropriate physics staffing for radiation treatment. Motivation for this work came from the age of current guidelines which predate the recent evolution in techniques and technology, and also several significant adverse incidents where a lack of physics staffing was identified as a contributing factor to excessive radiation exposure of patients. Methods: Guided by published times required per procedure, we developed an algorithm adaptable to local practice which estimates staffing requirements for medical physics with parameters derived from clinical procedures and service workload, equipment inventory, training, clinical development and administration. The predictive power was evaluated using data from 32 Canadian centres. This algorithm was used to model staffing requirements for the next 10 years to aid regional, institutional and educational program planning with consideration given to the “4Rs” of human resources planning: Requirements, Recruitment, Retention and Residency. Results: For centre-specific human resource planning, we propose a grid of coefficients addressing specific workload factors for each group. For larger scale planning, case-based ratios were determined at 260, 300 and 600 annual radiotherapy cases for medical physicists, dosimetrists and electronics technologists respectively. Assuming a 2.5% growth in incidence of cancer and stable utilisation, our supply and demand model predicts a requirement for an additional 39 medical physicists for Ontario by the year 2020. If an additional 3% annual growth in radiation therapy utilisation is included, the number rises to 87. Conclusions: We describe a robust algorithm to determine medical physics staffing levels adaptable to centre-specific workload and evolving local radiation treatment practice. Although annual caseload has been used in the past as a major parameter for global physics staffing determination, our results indicate that local clinical services and equipment as well as academic activity cause significant deviations from predictions based solely on caseload.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".