Can Prenatal Diagnosis of Total Anomalous Pulmonary Venous Return (TAPVR) Using Routine Fetal Ultrasound be Improved? A Case-Control Study
Bibliographic record
Abstract
Objectives: To determine the most common fetal ultrasound markers of total anomalous pulmonary venous return (TAPVR) during mid-trimester ultrasound using standardly obtained images and evaluate the performance of diagnostic algorithms for improving prenatal diagnosis. Methods: This was a matched case-control study at a regional referral centre (2005 to 2019). Cases of TAPVR were matched to controls 1 : 4 by date of birth and biologic sex. Postprocessing review of stored fetal ultrasound images was performed by two blinded and independent observers in a standardized fashion using nine sonographic markers: (i) left/right heart disproportion; (ii) abnormal distribution of great vessels; (iii) pulmonary vein entry into the left atrium (LA); (iv) confluence behind the LA; (v) abnormal coronary sinus; (vi) absence of the Coumadin ridge; (vii) aortic diameter; (viii) distance between LA and aorta; and (ix) post-LA space index >1.27. Descriptive and inferential statistics were used to present results and compare cases and controls. Diagnostic algorithms were compared by sensitivity/specificity. Results: =0.006). A diagnostic algorithm stratifying on absence of pulmonary veins followed by an abnormal Coumadin ridge, can correctly identify cases of TAPVR with high specificity (90.5%) and moderate sensitivity (61.9%). Conversely, a diagnostic algorithm using the presence of any 3 abnormal markers had improved specificity (94.1%) but poorer sensitivity (23.8%). Conclusions: Using standardly obtained images from routine fetal ultrasound, improved prenatal detection of isolated TAPVR is possible. A standardized diagnostic approach can be highly specific for fetal TAPVR, however, algorithms that are sufficiently sensitive for screening in the general population are still needed.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".