Ultrasonographic Estimation of Ventricular Volume in Infants Born Preterm with Posthemorrhagic Ventricular Dilatation: A Nested Substudy of the Randomized Controlled Early Versus Late Ventricular Intervention Study (ELVIS) Trial
Bibliographic record
Abstract
OBJECTIVE: To study the potential role of ventricular volume (VV) estimation in the management of posthemorrhagic ventricular dilatation related to the need for ventriculoperitoneal (VP)-shunt insertion and 2-year neurodevelopmental outcome in infants born preterm. STUDY DESIGN: We included 59 patients from the Early vs Late Ventricular Intervention Study from 4 participating centers. VV was manually segmented in 209 3-dimensional ultrasound scans and estimated from 2-dimensional ultrasound linear measurements in a total of 1226 ultrasounds. We studied the association of both linear measurements and VV to the need for VP shunt and 2-year neurodevelopmental outcome in the overall cohort and in the 29 infants who needed insertion of a reservoir. We used general estimating equations to account for repeated measures per individual. RESULTS: correctly classified 79.31% with an area under the curve of 0.76 (CI 95% 0.74-0.79). Maximum VV (β = 0.027; P = .012) together with VP shunt insertion (β = 3.773; P = .007) and gestational age (β = -0.273; P = .0001) were related to cognitive outcome at 2 years. Maximum ventricular index and anterior horn width before reservoir insertion were independently associated with the need of VP shunt and the proposed threshold groups in the Early vs Late Ventricular Intervention Study trial were associated with long-term outcome. CONCLUSIONS: Pre-reservoir VV measurements were associated with the need for VP-shunt insertion and 2-year cognitive outcome among infants born preterm with posthemorrhagic ventricular dilatation. TRIAL REGISTRATION: ISRCTN43171322.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".