Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it