Qualitative Assessment of Foetal Lung Size in Left Congenital Diaphragmatic Hernia Using Ultrasound and MRI: A Retrospective Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To evaluate the reliability of qualitative foetal lung size assessments on ultrasound (US) and MRI in left congenital diaphragmatic hernia (CDH) and their correlation with quantitative metrics and neonatal mortality. DESIGN: Retrospective cohort study. SETTING: Single tertiary center, 2008-2020. POPULATION: A total of 103 cases of prenatally diagnosed isolated left CDH underwent postnatal active care. METHODS: Two independent reviewers performed qualitative foetal lung size assessments on US and MRI. Interrater agreement was assessed, and correlations were determined between qualitative assessments, quantitative metrics (observed-to-expected lung-to-head ratio [o/e LHR] and observed-to-expected total Foetal lung volume [o/e TFLV]), and neonatal mortality. MAIN OUTCOME MEASURES: Interrater agreement and correlation between qualitative and quantitative assessments of lung size and neonatal mortality. RESULTS: A total of 74 cases with both US and MRI imaging were included. Interrater agreement for qualitative lung size assessment was strong for US (weighted kappa: 0.80, 95% CI 0.68-0.93) and moderate for MRI (Cohen's kappa: 0.48, 95% CI 0.30-0.66). Both modalities showed a strong correlation between qualitative and quantitative lung size assessments. On US, qualitative and quantitative assessments had similar associations with neonatal mortality (Spearman's correlation: 0.44 for each reviewer vs. 0.49). On MRI, quantitative metrics correlated more strongly with neonatal mortality than qualitative assessment (Cramér's V: 0.44 vs. 0.34-0.35). CONCLUSIONS: Qualitative foetal lung size assessment, by US more so than MRI, is a reliable and reproducible tool that correlates with established quantitative metrics and neonatal mortality. These findings support its role as a complementary method for prenatal risk stratification in left CDH.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".