Reproducibility of fetal lung‐to‐head ratio in left diaphragmatic hernia across the <scp>N</scp>orth <scp>A</scp>merican <scp>F</scp>etal <scp>T</scp>herapy <scp>N</scp>etwork (NAFTNet)
Bibliographic record
Abstract
Abstract Objective To determine the antenatal sonographic lung area measurement method in left congenital diaphragmatic hernia (CDH) with the highest interrater agreement among North American Fetal Therapy Network (NAFTNet) centers within and outside the fetoscopic tracheal occlusion (FETO) consortium and in comparison with a European “expert” reviewer (ER). Methods Nineteen members from nine FETO consortium centers and 29 reviewers from 17 non‐FETO centers reviewed ultrasound clips of the chest from 13 fetuses with isolated left CDH and were asked to select a static plane for lung area measurement using anteroposterior (AP), longest, and trace methods. Interrater agreement in lung area measurements was determined using intraclass correlation coefficient (ICC). Bland‐Altman analysis was used to evaluate mean difference (bias) between NAFTNet reviewers and ER. Results Among FETO centers, agreement was highest using trace (ICC 0.94; 95% CI, 0.83‐0.98), followed by longest (ICC 0.89; 95% CI, 0.75‐0.97) and lowest for A‐P (ICC 0.83; 95% CI, 0.67‐0.94). Similar trends were noted in non‐FETO centers. When compared with ER, bias was lowest for trace: 14 ± 38 mm2 and 19 ± 36 mm2 for FETO and non‐FETO centers, respectively. Conclusion The trace method demonstrated the highest interrater agreement and lowest bias for lung area estimation in left CDH across NAFTNet.
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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 | 0.023 | 0.065 |
| 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 | 0.001 |
| 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".