Fetal diagnosis and management of pulmonary artery sling: A case series
Bibliographic record
Abstract
OBJECTIVE: Pulmonary artery sling is a rare congenital anomaly accounting for 2% of all patients with vascular anomalies that cause airway obstruction. In the normal heart, the left (LPA) and right (RPA) pulmonary arteries arise in the intrapericardial space. However, in the pulmonary artery sling, the LPA trunk arises in the extrapericardial space from the posterior aspect of the mid RPA and courses posterior to the trachea causing tracheal compression and, at times, bronchial compression. While a full spectrum of congenital cardiac pathology can be identified before birth, only a few case reports document the prenatal diagnosis of an Left pulmonary artery sling (LPAS). METHOD: We retrospectively identified all cases of prenatal LPAS from three Canadian fetal cardiology centers (2015-2022). RESULTS: Using the 3-vessel-tracheal view via fetal echocardiography (FE), four fetuses from three pregnancies demonstrated abnormal origin of the LPA from RPA and echogenic trachea. In one of two affected monochorionic twins coronal imaging demonstrated a significant narrowing of the large airways consistent with significant airway obstruction. CONCLUSION: Prenatal detection of LPAS by FE is possible and should prompt an evaluation for airway obstruction in the coronal view. Investigating associated lesions and genetic testing are recommended for informed shared decision making.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".