Not Every Low-Dose Is Low-Dose: Impact of Revising Low-Dose CT Protocol on Mean Effective Radiation Exposure
Bibliographic record
Abstract
Introduction: According to the American Urological Association imaging guidelines, patients presenting with renal colic should undergo low-dose (LD) rather than standard-dose (SD) noncontrast CT. The aim of the present study was to assess how often physicians ordered LD CT scans and to calculate mean effective radiation exposure (ERE) from CT scans from dose length products, and determine mean cumulative ERE over 1-year follow-up period. Methods: After obtaining ethics approval, a retrospective chart review was conducted for patients with renal colic presenting to the emergency department between August 1, 2015 and July 31, 2016 (Phase I) and between April 1, 2019 and October 1, 2019 (Phase II). All imaging studies performed within 1-year of initial presentation were cataloged. Results: In Phase I, 146 patients, with mean age of 51 years and mean body mass index (BMI) of 28.6 kg/m 2 , underwent 220 CT scans. In Phase II, 225 patients, with mean age of 55 years and mean BMI of 26.7 kg/m 2 , underwent 273 CT scans. Urologists were the only physicians ordering LD CT scans and they ordered significantly more LD than SD CT scans (71.3% vs 28.7%, p < 0.001). In Phase II, after revision of LD CT scan protocol in March 2019, the mean ERE per LD CT significantly decreased (6.5 vs 1.6 mSv, p < 0.001). In addition, there were significant differences in mean ERE from LD CT scans between two hospitals in the same health system (1.6 vs 7.8 mSv, p < 0.001). The mean cumulative ERE in Phase II over the 1-year period was 19.3 mSv, with 6.9% of patients exceeding 50 mSv. Conclusions: Although LD CT scans are being ordered, a small percentage of patients continue to exceed the 50 mSv annual threshold. It is important to keep track of mean ERE of LD CT scans and collaborate with medical physicists and the diagnostic imaging department to further refine LD CT scan protocols since not every low-dose is low-dose.
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".