Reducing radiation exposure during procedures performed in the electrophysiology laboratory
Bibliographic record
Abstract
Abstract Introduction Expert societies recently published strong recommendations to reduce the exposure of patients and staff to ionizing radiation (IR) during interventional and electrophysiology (EP) procedures. However, adherence to these guidelines remains difficult and the impact of implementing such recommendations is poorly characterized. Methods and results We conducted a single‐center cohort study to quantify radiation exposure over time in three EP laboratories at the Montreal Heart Institute during 5,546 consecutive procedures from 2012 to 2015 by 11 primary operators. Overall, 2,618 (47.2%) procedures were catheter‐based and 2,928 (52.8%) were device interventions. Interventions to reduce radiation exposure included educational initiatives to raise awareness (i.e., limiting cine acquisition, patient position, table height), slower frame rate, lower radiation dose per pulse, collimation, and integration with 3‐D mapping systems and/or MediGuide technology. An 85% reduction in IR exposure was observed from 2012 to 2015, with the mean dose‐area‐product (DAP) decreasing from 7.65 ± 0.05 Gy·cm 2 to 1.15 ± 0.04 Gy·cm 2 (P < 0.001). This was true for catheter‐based procedures (mean DAP 16.99 ± 0.08 to 2.00 ± 0.06 Gy·cm 2 , P < 0.001) and device interventions (mean DAP 4.18 ± 0.06 to 0.64 ± 0.05 Gy·cm 2 , P < 0.001). The median effective dose of IR recorded per quarter by 282 cervical dosimeters on EP staff decreased from 0.57 (IQR 0.18, 1.03) mSv in 2012 to 0.00 (IQR 0.00, 0.19) mSv in 2015, P < 0.001. Conclusion Enforcing good clinical practices with simple measures and low‐dose fluoroscopy settings are highly effective in reducing IR exposure in the EP lab. These promising results should encourage other EP labs to adopt similar protective measures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".