Perinatal predictors of neurodevelopmental outcomes in high-risk neonates
Bibliographic record
Abstract
Background: Thanks to advancements in neonatal medicine, perinatal morbidity has been significantly reduced, but the number of high-risk neonates continues to rise. Efforts to predict neurodevelopmental outcomes at an early age remain limited. The aim of this study was to analyze perinatal predictors of neurodevelopmental outcomes in high-risk neonates. Methods: A prospective, longitudinal two-year study was conducted at the Pediatric Clinic of the University Clinical Center in Tuzla. The study included 151 neonates, with 99 in the test group (with known perinatal risk factors) and 52 in the control group (without risk factors). Early neurodevelopment was assessed using the Alberta Infant Motor Scale (AIMS). Standard statistical methods were applied for data processing. The study was approved by the Institutional Ethics Committee. Results: Of the 151 neonates observed, 108 (71.5%) had normal neurodevelopment at 18 months, 29 (19.2%) had mild disorders, and 14 (9.3%) had developmental delays. In the group with suboptimal neurodevelopment, significantly more twin pregnancies, health problems during pregnancy, unnatural births, artificial fertilization, and pregnancy complications were recorded. In neonates, there were significantly more premature births, hypoxic-ischemic encephalopathy, and intracranial hemorrhages. Significant correlations were found between the mother's age and parity and delayed neurodevelopment. Additionally, correlations were found between birth weight, gestational age, Apgar score, length of hospitalization, and NICU stay with neurodevelopmental delay. Gestational age and the Apgar score at 1 minute showed significant negative predictive value for neurodevelopmental delay. Conclusion: Prematurity and perinatal asphyxia remain the greatest risks for adverse neurodevelopmental outcomes in neonates. These factors should be the focus of continued medical research and clinical practice. Neonates at the highest risk of developmental delay and their families should be prioritized for early identification, long-term follow-up, and timely interventions.
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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 | 0.004 |
| 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 | 0.000 | 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".