Multidetector cardiac tomography: A useful tool before cardiac resynchronization therapy
Bibliographic record
Abstract
Background: Left ventricular lead placement in a suitable coronary vein is a key determinant of responsiveness to cardiac resynchronization therapy (CRT). Multidetector cardiac tomography (MDCT) is a non-invasive alternative to depict cardiac venous anatomy although coronary sinus (CS) retrograde venography (RV) is the gold standard. The aim of this study was to evaluate the accuracy of MDCT to determine the presence of CS tributaries before CRT. Methods: A retrospective analysis of 41 consecutive patients eligible to CRT was performed. MDCT was assessed in all patients before CRT and RV was achieved in 39 patients. Both methods evaluated the presence of the inferior interventricular vein (IIV), posterior vein (PV) and lateral main vein (LMV). CS ostium diameter and distance between the CS ostium and right atrium (RA) lateral wall were also measured. Results: The IIV was identified in 100% of MDCT and in 43.6% of RV. In comparison to RV, the MDCT’s sensitivity to identify PV and LMV was 100% for both, kappa coefficient of 0.792 (CI 95% 0.46–0.93) and 0.69 (CI 95% 0.46–0.91), respectively. There was no significant difference between ischemic and non-ischemic patients regarding the presence of PV or LMV. Median CS antero-posterior diameter was 10.3 mm (IQR 7.5–13) and supero-inferior was 14.1 mm (IQR 11.5–17) (p < 0.01). A positive correlation (p < 0.001) between echocardiographic RA area and the distance from CS ostium to the RA lateral wall in the MDCT was observed. Conclusions: MDCT is as accurate as RV to depict CS and its tributaries (IIV, PV, LMV), and it could be useful as a non-invasive technique before CRT.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".