SU-E-T-103: Development and Implementation of Web Based Quality Control Software
Bibliographic record
Abstract
Purpose: Historically many radiation medicine programs have maintained their Quality Control (QC) test results in paper records or Microsoft Excel worksheets. Both these approaches represent significant logistical challenges, and are not predisposed to data review and approval. It has been our group's aim to develop and implement web based software designed not just to record and store QC data in a centralized database, but to provide scheduling and data review tools to help manage a radiation therapy clinics Equipment Quality control program. Methods: The software was written in the Python programming language using the Django web framework. In order to promote collaboration and validation from other centres the code was made open source and is freely available to the public via an online source code repository. The code was written to provide a common user interface for data entry, formalize the review and approval process, and offer automated data trending and process control analysis of test results. Results: As of February 2014, our installation of QAtrack+ has 180 tests defined in its database and has collected ∼22 000 test results, all of which have been reviewed and approved by a physicist via QATrack+'s review tools. These results include records for quality control of Elekta accelerators, CT simulators, our brachytherapy programme, TomoTherapy and Cyberknife units. Currently at least 5 other centres are known to be running QAtrack+ clinically, forming the start of an international user community. Conclusion: QAtrack+ has proven to be an effective tool for collecting radiation therapy QC data, allowing for rapid review and trending of data for a wide variety of treatment units. As free and open source software, all source code, documentation and a bug tracker are available to the public at https://bitbucket.org/tohccmedphys/qatrackplus/.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.013 |
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".