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Record W4410193701 · doi:10.1002/uog.29233

Variability in antenatal prognostication of congenital diaphragmatic hernia by magnetic resonance imaging across the North American Fetal Therapy Network ( <scp>NAFTNet</scp> )

2025· article· en· W4410193701 on OpenAlexaff
Nimrah Abbasi, Dilkash Kajal, Anthony Johnson, Greg Ryan, S. Lee, Prakesh S. Shah, Erin E. Perrone

Bibliographic record

VenueUltrasound in Obstetrics and Gynecology · 2025
Typearticle
Languageen
FieldMedicine
TopicCongenital Diaphragmatic Hernia Studies
Canadian institutionsUniversity Health NetworkWomen's College HospitalUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsMedicineCongenital diaphragmatic herniaConcordanceMagnetic resonance imagingPulmonary hypoplasiaGestational ageHerniaLung volumesRadiologyCoefficient of variationFetusDiaphragmatic breathingNuclear medicineLungPregnancyInternal medicinePathology

Abstract

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OBJECTIVE: To evaluate the variability in magnetic resonance imaging (MRI)-based parameters used for fetal lung volume estimation in the prediction of pulmonary hypoplasia and the degree of liver herniation in cases of antenatally diagnosed left congenital diaphragmatic hernia (CDH) across North American Fetal Therapy Network (NAFTNet) centers. METHODS: In this study, 14 NAFTNet radiologists reviewed MRI exams of 15 cases of left CDH of variable severity, eight of which had liver herniation confirmed at surgery. Images were obtained at a median gestational age of 29.3 (range, 25.0-37.6) weeks, between 2020 and 2022. All participants were asked to rate image quality using a scale of 1-4 (where 1 represents excellent quality and 4 represents poor quality (unable to perform measurements)) and to determine the observed-to-expected total fetal lung volume (o/e-TFLV) using the formulae of Rypens et al. and Meyers et al., the percent predicted lung volume (PPLV), the presence or absence of liver herniation and the percentage of liver herniation (%LH). Fleiss' κ was used to assess inter-rater agreement for image-quality ratings. Concordance between participants was evaluated by determining a coefficient of variation (CV), with CV < 30 defined as acceptable. Additionally, the variation of individual participant's assessment of a case from the group average was also assessed. Data were also evaluated by center case volume, for which high volume was indicated by ≥ 15 CDH cases/year and low volume was indicated by < 15 CDH cases/year managed prenatally. RESULTS: Overall, there was acceptable concordance for o/e-TFLV among reviewers using the formula of either Rypens et al. or Meyers et al. (median CV, 24 (interquartile range (IQR), 19-34)). Slightly lower but acceptable concordance was noted for PPLV (median CV, 26 (IQR, 18-42)). For the determination of liver herniation, most participants agreed with the final diagnosis at surgery in 14/15 cases; however, concordance was lowest among reviewers for the quantification of %LH (median CV, 46 (IQR, 44-53)). Among the three MRI exams rated as being of poor quality by the majority of participants, CV was higher for o/e-TFLV (median CV, 39) and PPLV (median CV, 43), indicating poor concordance among reviewers. No significant difference was noted in concordance among reviewers for the assessment of lung volume and liver herniation based on a center's CDH volume. CONCLUSION: Noticeable variability with acceptable agreement was noted for o/e-TFLV, PPLV and determination of liver herniation between NAFTNet radiologists from 14 centers in cases of left CDH. However, significant heterogeneity was noted for %LH. Concordance among reviewers was similar, irrespective of center case volume, highlighting the need for standardization of imaging protocols and CDH prognostication by MRI. © 2025 The Author(s). Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.250
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations2
Published2025
Admission routes1
Has abstractyes

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