A Radiographic Diagnostic Reference Level Survey Using Patient and Phantom Data
Bibliographic record
Abstract
A diagnostic reference level (DRL) survey was conducted for seven common radiographic projections across an integrated health region, covering 27 hospitals and clinics. The projections surveyed were Chest Posterior-Anterior (PA) and Lateral, Abdomen Supine and Upright, L-spine Anterior-Posterior (AP) and Lateral, and Pelvis. Dose area product (DAP) values were collected from patient examinations in 43 digital radiography (DR) rooms and in 18 conventional rooms which use computed radiography (CR). In each room, data were collected for between 10 and 20 patient exposures for each surveyed projection. In addition, for each projection and room, a DAP value was measured for the exposure of a uniform acrylic phantom of standardized thickness. DAP values in DR rooms were found to be significantly lower than in CR rooms (p < 0.05). Therefore, DR and CR rooms were analyzed separately. Based on the survey results, separate DRLs are presented for DR and CR rooms, for both patient and phantom DAP values. DRLs have been set at the 75th percentile values; 25th percentile and median values are also presented to characterize the range of observed values. When identifying which rooms were above the DRLs, the patient and phantom data identified a partially different set of rooms. Possible reasons for these differences, including uncertainties in the patient data, are discussed.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".