Assessment of pulmonary veins after atrio-pericardial anastomosis by cardiovascular magnetic resonance
Bibliographic record
Abstract
BACKGROUND: The atrio-pericardial anastomosis (APA) uses a pericardial pouch to create a large communication between the left atrium and the pulmonary venous contributaries in order to avoid direct suturing of the pulmonary veins during the repair of congenital cardiac malformations. Post-operative imaging is routinely performed by echocardiography but cardiovascular magnetic resonance (CMR) offers excellent anatomical imaging and quantitative information about pulmonary blood flow. We sought to compare the diagnostic value of echocardiography and CMR for assessing pulmonary vein anatomy after the APA. METHODS: This retrospective study evaluated all consecutive patients between October 1998 and January 2010 after either a primary or secondary APA followed by post-repair CMR. RESULTS: Of 103 patients who had an APA, 31 patients had an analyzable CMR study. The average time to CMR was 24.6 ± 32.5 months post-repair. Echocardiographic findings were confirmed by CMR in 12 patients. There was incomplete imaging by echocardiography in 7 patients and underestimation of pulmonary vein restenosis in 12, when compared to CMR. In total, 19/31 patients (61%) from our cohort had significant stenosis following the APA as assessed by CMR. Our data suggest that at least 18% (19/103) of all patients had significant obstruction post-repair. CONCLUSIONS: Echocardiography incompletely imaged or underestimated the severity of obstruction in patients compared with CMR. Pulmonary vein stenosis remains a sizable complication after repair, even using the APA.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".