Reduction in radiation exposure in cardiovascular computed tomography imaging: results from the PROspective multicenter registry on radiaTion dose Estimates of cardiac CT angIOgraphy iN daily practice in 2017 (PROTECTION VI)
Bibliographic record
Abstract
Aims: Advances of cardiac computed tomography angiography (CTA) have been developed for dose reduction, but their efficacy in clinical practice is largely unknown. This study was designed to evaluate radiation dose exposure and utilization of dose-saving strategies for contrast-enhanced cardiac CTA in daily practice. Methods and results: Sixty one hospitals from 32 countries prospectively enrolled 4502 patients undergoing cardiac CTA during one calendar month in 2017. Computed tomography angiography scan data and images were analysed in a central core lab and compared with a similar dose survey performed in 2007. Linear regression analysis was performed to identify independent predictors associated with dose. The most frequent indication for cardiac CTA was the evaluation of coronary artery disease in 89% of patients. The median dose-length product (DLP) of coronary CTA was 195 mGy*cm (interquartile range 110-338 mGy*cm). When compared with 2007, the DLP was reduced by 78% (P < 0.001) without an increase in non-diagnostic coronary CTAs (1.7% in 2007 vs. 1.9% in 2017 surveys, P = 0.55). A 37-fold variability in median DLP was observed between the hospitals with lowest and highest DLP (range of median DLP 57-2090 mGy*cm). Independent predictors for radiation dose of coronary CTA were: body weight, heart rate, sinus rhythm, tube voltage, iterative image reconstruction, and the selection of scan protocols. Conclusion: This large international radiation dose survey demonstrates considerable reduction of radiation exposure in coronary CTA during the last decade. However, the large inter-site variability in radiation exposure underlines the need for further site-specific training and adaptation of contemporary cardiac scan protocols.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".