Lung Hypoplasia in Fetuses with Skeletal Dysplasia Determined by Fetal Lung Weight: Which Ultrasound Measurement/Ratio Has the Highest Detection Rate
Bibliographic record
Abstract
INTRODUCTION: To determine lung hypoplasia in cases with fetal skeletal dysplasia based on the total lung weight at autopsy as the most accountable surrogate marker for pulmonary hypoplasia. METHODS: This retrospective cohort study included all pregnancies with antenatal diagnosis of skeletal dysplasia (2012-2018). We included only cases in which information on fetal biometry was available within 2 weeks before delivery and had autopsy and skeletal X-rays + molecular analysis using extracted fetal DNA. We compared the predictive accuracy of fetal sonographic body-proportional ratios (BPRs) including: (1) thoracic circumference-to-abdominal circumference ratio, (2) the femur length-to-abdominal circumference (FL/AC) ratio, (3) head circumference-to-abdominal circumference ratio, and (4) foot length-to-femur length ratio. Lung hypoplasia was defined as total lung weight below -2 SD from the expected mean for gestational age. RESULTS: Fifty three pregnancies with antenatal diagnosis of skeletal dysplasia underwent autopsy included. Lung hypoplasia was determined in 34 (64.1%). Median of gestational age at last sonographic assessment was 21.3 (19.9-24.9) weeks. FL/AC ratio demonstrated the highest area under the curve of 0.817 (95% CI: 0.685-0.949; p < 0.0001). FL/AC ≤0.1550 demonstrated the highest detection rate of 88.2% along with the highest negative predictive value of 75%. CONCLUSION: Using a novel, more practical approach to predict lung hypoplasia in skeletal dysplasia, fetal sonographic BPRs and, specifically, FL/AC ratio demonstrate a high detection rate of lung hypoplasia.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".