Sci‐Fri AM: MRI and Diagnostic Imaging ‐ 02: Quality Improvement: Diagnostic Reference Levels for Interior Health CT exams – L‐Spine, Chest/Abdomen/pelvis, Abdomen/Pelvis, Head
Bibliographic record
Abstract
Diagnostic Reference Levels are used to optimize patient dose and image quality in the clinical setting. It is assumed that the majority of exams are of diagnostic quality, or the radiologists would request protocol adjustments. By investigating the dose indicator distributions from all scanners, the upper DRL can be set to the 75th percentile of the distribution and a lower DRL can be set to the 10th percentile. Scanners using doses consistently outside the upper/lower DRL range can be adjusted accordingly. 11 CT scanners, all contributing to the American College of Radiology Dose Index Registry (ACR DIR) were used in this study. Dose indicator data were compiled from the ACR DIR data and local DRLs established. Scanners with median doses outside the upper/lower DRL were followed‐up with. Using effective dose and exam volumes, collective dose was determined before and after protocol adjustments to evaluate the effect of this quality improvement effort. The quality initiative is complete for L‐spine and Chest/Abdomen/Pelvis exams and only initial surveys were completed for Head and Abdomen/Pelvis examsg. Median Scanner Dose reductions were 8.8 and 4.9 % for L‐spine and Chest/Abdomen/Pelvis exams, respectively, resulting with collective dose reductions of 0.7 and 3.2 person•Sv/yr. Follow‐up is ongoing for Abdomen/Pelvis and Head exams.
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.
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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.011 |
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".