Bibliographic record
Abstract
The University Health Network (UHN) maintains 30 CT X-ray systems at five hospital locations. The problem that was addressed was how to develop and implement a CT QA Program at the University Health Network. This program was developed to satisfy the needs for a standardized annual testing of CT dose and image quality parameters. Appropriate image quality test phantoms were selected and evaluated. This topic deals with the problem of how to develop and implement a CT QA Program for the CT systems at the University Health Network. The project will deal with the selection of image quality and dose phantoms, establishing baseline data derived from the phantoms and storing the data for future reference. Another challenge of the project was to develop a quality assurance program that will satisfy the corporate vision of the University Health Network. This vision is concerned with achieving global impact. The work done in this report will be relevant to Canadian standards and incorporate global standards as well. A comprehensive CT QA Program was then developed and implemented. The program was used to report on CT image quality and dose parameters. It is recommended that the AIM CT QA Program be used at other hospitals to provide comprehensive image quality evaluation, dose monitoring and structured CT reporting.
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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 | 0.023 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".