Simple prenatal imaging predictors for postnatal cerebrospinal fluid diversion surgery in fetuses undergoing in utero surgery for spina bifida
Bibliographic record
Abstract
OBJECTIVES: To determine simple prenatal imaging parameters that can easily be acquired to predict the need for postnatal CSF diversion (PCD) surgery in fetuses undergoing open fetal surgery for open spina bifida (OSB). METHODS: All fetuses with OSB that underwent open fetal surgery between June 2017 and June 2021 with available follow-up outcomes were included. Imaging parameters including clivus-supraocciput angle (CSA) on magnetic resonance imaging, transcerebellar diameter (TCD) and lateral ventricle size (Vp) on ultrasound (US), were collected pre- and postoperatively. The requirement for PCD surgery was determined at 1 year of age. The predictive strength of each parameter was determined by Receiver Operating Characteristic curve analysis. RESULTS: Among 36 babies eligible for the analyses, 41.7% required PCD by one year of age. Pre-operative Vp (AUC 0.71; 95% confidence interval [CI] 0.54-0.88; p = 0.03), TCD (AUC 0.72; 95% CI 0.55-0.89; p = 0.02) and CSA (AUC 0.72; 95% CI 0.51-0.93; p = 0.04) were fair predictors for PCD surgery. After fetal surgery, TCD (AUC 0.93; 95% CI 0.83-1.00; p < 0.0001) and CSA (AUC 0.94; 95% CI 0.83-1.00; p = 0.0005) were outstanding predictors of PCD, whereas post-operative Vp was a fair predictor (AUC 0.71, 95% CI 0.54-0.88, p = 0.03). CONCLUSION: Post-operative CSA and TCD were outstanding predictors for the need for PCD surgery.
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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 | 0.001 | 0.009 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".