Reducing Radiation Exposure During CRT Implant Procedures: Single‐Center Experience With Low‐Dose Fluoroscopy Settings and a Sensor‐Based Navigation System (MediGuide)
Bibliographic record
Abstract
INTRODUCTION: Cardiac resynchronization therapy (CRT) implant procedures are often complex and prolonged, resulting in substantial ionizing radiation (IR) exposure to the patient and operator. We assessed the impact of lower-dose fluoroscopy settings and a sensor-based electromagnetic tracking system (MediGuide™, MDG) on reducing IR exposure during CRT implantation. METHODS: A single-center 2-group cohort study was conducted on 348 consecutive patients, age 66.4 ± 11.0 years, 80.4% male, with CRT implant procedures from 2013 to 2015. Patients were arbitrarily assigned to MDG (N = 239) versus no MDG (N = 109) guidance. Lower-dose fluoroscopy settings were adopted in January 2015 (3 instead of 6 fps; 23 instead of 40 nGy/pulse; N = 101). RESULTS: , P < 0.001). Procedures with MDG were shorter (96 minutes vs. 123 minutes, P < 0.001) and associated with a trend towards a higher success rate (94.6% vs. 89.0%, P = 0.062), with fewer coronary sinus cannulation failures (2.1% vs. 6.4%, P = 0.040). CONCLUSION: Low-dose fluoroscopy settings are highly effective (>50%) in reducing IR exposure during CRT implant procedures. When combined with MDG, >95% reduction in IR exposure is achieved. Moreover, MDG shortens procedural duration and may improve acute procedural outcomes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".