EP31.03: Are we accurately predicting outcome in CDH?
Bibliographic record
Abstract
The goal of our study was to perform an audit of the performance and utility of US LHR, US o/e LHR, MRI o/e TFLV in predicting morbidity and mortality in CDH. We performed a retrospective review of cases of prenatally diagnosed congenital diaphragmatic hernia over a five-year period. US lung area was assessed retrospectively by two MFM physicians on digitally stored image using the IMPAX free-form icon. MRI assessment of fetal lung volumes was performed by one of two senior pediatric radiologists using a half-Fourier acquisition single shot turbo spin echo sequence. Data was assessed qualitatively and quantitatively (by Pearson product-moment correlations for assessment of correlation between the diagnostic imaging parameters and the outcomes of interest). Overall, 31 cases of CDH were documented. After applying exclusion criteria, 78% had left CDH and 22% had right CDH. On retrospective grading of ultrasound images, 66% of images were felt to be of moderate quality while about 33% of low quality. There was no statistically significant correlation between ultrasound o/e LHR/LHR and neonatal mortality. There was a trend towards increased mortality with lower MRI o/e FLV ratio (r = -.443, p = .086). Training in accurate assessment of fetal lung area affects the previously reported correlation between ultrasound lung parameters and neonatal outcome. Our results are affected by our cohort size and the US images. Fetal position, proximity measured lung to transducer, etc can affect prognostication. Since our study, stricter US criteria are being applied to images used for prognosticating in our centre.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".