Web-based system for quality assurance of radiation oncology equipment and procedures
Bibliographic record
Abstract
In a radiation therapy department, several periodic (daily, monthly, quarterly, yearly, etc.) and on-request quality control tests are performed as part of the quality assurance program. The lack of a commercial solution to unify all these tests in one single system was the motivation for this project. The goal of this thesis work was to develop a web-based quality assurance software tool for the radiation oncology division of the Jewish General Hospital that would be easily expendable and manageable. The tool that was created allows easy access to the tests through a simple web interface yet allowing advanced management of user rights, processing of complex numerical data, warning users through email alerts and reports, scheduling tests, keeping trends of the test results and providing safe storage for the collected data. Our system is based on Drupal, an open source web content management system. Several customizations were done to the basic Drupal system to adapt it to our needs: several scripts and specialized modules were used to enter and analyse collected data (text and images) as well as exchange data with the radiotherapy electronic medical record database. In this thesis work we have selected and implemented in our system a limited collection of quality control tests (9) that are representative of all types of tests that are performed in a radiotherapy clinic, as a full implementation would be beyond the time frame of this project. They are the bases for a future complete implementation and can be used as a model for other similar tests. The implemented tests are now being introduced in the clinic simplifying data entry, access, and analysis.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.034 |
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".