Po‐Thur Eve General‐35: Official statistics generated using a commercial R&V system in the Canadian context
Bibliographic record
Abstract
In Quebec every radiotherapy department has to present official statistics to the government known has technical units. All actions taken by therapists generate technical units roughly related to the amount of time needed to complete the task. Technical units are defined per category and task by the officials. A commercial record and verify system, VARiS version 7.2 from Varian Medical Systems, has successfully been configured to automatically capture the required technical units in Quebec. VARiS is normally installed preconfigured to collect charges in the United States using Medicare codes and charges through the application Activity Capture. This program interacts with other applications of the R&V system to greatly ease the process of confirming chargeable activities as they are completed. Replacing the Medicare codes with the Quebec codes in the administration module was the most demanding task. At treatment machines, a window will automatically appear when a treatment session is completed to confirm the activity to be charged. The application will suggest the same codes that were completed the last time. In the case portal images were acquired, the configured code will be suggested even though the code was not used in the prior session. Technical units generated elsewhere, for example in dosimetry, are computed using a single predefined template attached to every patient. The therapist searches the template for the proper code and manually completes it so that it is taken in to account in the patient's statistics.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.040 | 0.008 |
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".