Comparison of image quality indicators among mammography facilities in Ontario.
Bibliographic record
Abstract
OBJECTIVE: To compare the technical aspects of image quality of mammography machines in facilities across Ontario. METHODS: Eight mammographic physics consultants took measurements and calculated accreditation phantom scores, mean glandular dose, entrance exposure, average optical density, half value layer and the limiting resolution of the imaging chain for 100 mammography machines across Ontario. RESULTS: Of the 100 machines, 39 were affiliated with the Ontario Breast Screening Program (OBSP), and the remaining 61 were applying to become OBSP affiliates. All of the OBSP facilities and 32 of the applicants were accredited with the Mammography Accreditation Program (MAP) of the Canadian Association of Radiologists (CAR). All OBSP facilities had passing phantom scores, and 84% of the applicants with CAR accreditation and 79% of those without had passing phantom scores. The mean glandular dose was 1.5 (range 1.0-1.9, standard deviation [SD] 0.22) mGy for OBSP facilities, 1.3 (0.8-1.8, SD 0.29) mGy for CAR accredited and 1.4 (0.9-1.9, SD 0.24) mGy for nonaccredited applicants. Mean entrance exposures were 798 (540-1280, SD 135) mR, 717 (430-980, SD 153) mR and 770 (520-930, SD 116) mR for the OBSP, accredited and nonaccredited facilities respectively. There were no appreciable differences in the mean optical densities (approx. 1.62 OD), mean half-value layers (approx. 0.34 mm Al) or mean resolutions measured with the large focal spot (nominal size of 0.3 mm) either parallel (approx. 16 line pairs/mm) or perpendicular (approx. 14 line pairs/mm) to the cathode-anode axis of the x-ray tube, among the 3 types of facilities. CONCLUSION: Image quality is increased and variability is decreased in facilities participating in a province-wide screening program.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".