Sci-Fri AM(1): Imaging-05: Setting Local Diagnostic Reference Levels
Bibliographic record
Abstract
OBJECTIVE To establish local diagnostic reference levels (DRLs) for typical radiographic examinations in a fully digital imaging institution, and to compare these to values published in Health Canada's Safety Code 35A. METHODLOGY Standard radiographic projections performed in twenty radiographic rooms at six different hospital sites were evaluated. Six rooms employed Digital (DR) units and fourteen rooms employed Computed Radiography (CR) systems. Except for four CR rooms in which technical factors were set manually, all rooms employed automatic exposure control. Analysis included data of 342 average adult patients and anthropomorphic phantoms. Entrance surface doses were calculated from tube radiation output measurements. RESULTS Typically, average patient doses for similar examinations were lower in the DR rooms than the CR rooms by factors ranging from of 1.2 to 3.1. Variations for the same examination performed in different rooms DR rooms were relatively small, but ranged by up to 7.9 times for CR imaging. Initial efforts to understand the reasons for the variations focused on Chest, Abdomen and Lumbar Spine examinations. The major reason identified was varying reference Exposure Index values which were accepted for images of diagnostic quality. CONCLUSION For Chest, Abdomen, and Lumbar Spine radiographic examinations local DRLs for DR systems were set lower than that for CR systems, and all DRL's were set lower than those recommended in Safety Code 35A, except for CR chests. Future work includes further optimization of all standard radiographic and fluoroscopic examinations.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.018 | 0.010 |
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".