Comparison of non-gated vs. electrocardiogram-gated 64-detector-row computed tomography for integrated electroanatomic mapping in patients undergoing pulmonary vein isolation
Bibliographic record
Abstract
AIMS: To compare non-gated vs. electrocardiogram (ECG)-gated 64-detector-row computed tomography (MDCT) of the left atrium (LA) for integrated electroanatomic mapping (EAM) in patients with paroxysmal atrial fibrillation (AF). METHODS AND RESULTS: Twenty-nine consecutive patients with paroxysmal AF underwent MDCT prior to pulmonary vein isolation (PVI). All patients were in sinus rhythm both during CT imaging and PVI. Multi-detector-row computed tomography was performed in 15 patients without ECG-gating (non-gated MDCT) and in 14 patients with retrospective ECG-gating (ECG-gated MDCT). Image quality of LA reconstructions from MDCT was rated on a five-point scale (from 1 = excellent to 5 = segmentation failed). Registration error between LA geometry obtained from EAM and MDCT was calculated as the mean distance between EAM points and MDCT surface. In all patients, LA was successfully segmented from MDCT data. The segmentation process took 2:31 +/- 0:54 min for non-gated MDCT and 2:36 +/- 0:47 min for ECG-gated MDCT (P = 0.8). Image quality scores of LA reconstructions from non-gated and ECG-gated MDCT were 1.3 +/- 0.6 and 1.4 +/- 0.7, respectively (P = 0.76). There was no significant difference in the registration error between non-gated and ECG-gated MDCT (1.8 +/- 0.2 vs. 1.9 +/- 0.3 mm, respectively; P = 0.6). The radiation dose of non-gated MDCT was significantly lower compared with ECG-gated MDCT (4.6 +/- 1.4 vs. 13.4 +/- 3.6 mSv, respectively; P < 0.001). CONCLUSION: Non-gated MDCT depicts LA with appropriate image quality for integrated EAM, while exposing patients to substantially lower radiation dose compared with ECG-gated MDCT.
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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".