SU‐E‐T‐250: Results of a Survey to Assess the Current Status of In‐Vivo Dosimetry in Canada
Bibliographic record
Abstract
Purpose: To assess the current status of in‐vivo dosimetry in the Canadian cancer centers. Methods: A survey on the use of in‐vivo dosimetry was performed between July and September 2010. The survey was sent to 39 Canadian cancer centers and it was composed of 16 questions including questions on the use of in‐vivo dosimetry as well as demographics of the centers. A total of 34 centers completed the survey. Results: The survey showed that the Ontario and Nova Scotia have the largest number staff per clinic (99 and 75, respectively). Alberta and Manitoba have the largest number of medical physicist per clinic (15 and 12, respectively). The majority of the centers (79%) answered that they perform in‐vivo dosimetry to some extent. However, none of the centers perform daily or weekly in‐vivo dose measurements for individual patients, except for total body irradiation and total skin irradiation treatments. Most of the centers (74%) reported that they use a tolerance level of 5% or higher in their in‐vivo dosimetry programs. The majority of the centers (85%) answered that in‐vivo dose measurements are performed by the medical physicists other than physics assistants, dosimetrists and therapists. As pointed out by the centers, the major drawbacks and difficulties involved in the use of in‐vivo dosimetry included increased treatment and staff time. Conclusion: We assessed the current status of in‐vivo dosimetry in the Canadian cancer clinics. The results of this survey will serve as a documentation of the current status of the practice of in‐vivo dosimetry in Canada. Then, in the future such results will serve as a reference to assess further changes, developments and improvements in the field of in‐vivo dosimetry in Canada.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".