Validation of the vasoactive-ventilation-renal score in extreme preterm neonates
Bibliographic record
Abstract
Objectives To validate Vasoactive-Ventilation-Renal (VVR) score in extremely low gestational age neonates (ELGANs) as a predictor of mortality and morbidity by assessing its association with clinical outcomes. Study Design This was a secondary analysis of data from a randomized controlled trial including neonates born 23 0 –28 6 weeks’ gestation admitted to a Canadian tertiary-level neonatal intensive care unit between February 2019 and December 2021. VVR scores were measured at set intervals. Outcomes included mortality, intraventricular hemorrhage (IVH), bronchopulmonary dysplasia (BPD), necrotizing enterocolitis, patent ductus arteriosus, retinopathy of prematurity, mechanical ventilation duration, and length of hospital stay. Multivariate logistic regression analysis and receiver operating characteristic (ROC) curves were used to determine the association between VVR scores and clinical outcomes. Results Data from 132 neonates were analyzed. The mean (SD) gestational age was 26.5 (1.5) weeks, and the mean (SD) birth weight was 933 (243) grams. A VVR score >48 was significantly associated with severe IVH (AOR: 5.8, 95% CI: 1.2–28.9, p = 0.03), BPD (AOR: 8.8, 95% CI: 1.1–72.4, p = 0.044), prolonged mechanical ventilation (>71 days) (AOR: 6.86, 95% CI: 1.6–30, p = 0.01), and extended hospital stay (>150 days) (AOR: 6.19, 95% CI: 1.4–26.4, p = 0.01). No significant associations were observed with mortality or other outcomes. ROC curves analysis demonstrated good predictive performance of VVR score at 7 days for these adverse outcomes. Conclusion The VVR score at 7 days is a reliable predictor of significant adverse outcomes, including severe IVH and BPD, in ELGANs. Further studies in larger, diverse populations are warranted to confirm these findings.
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 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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".