Does a Split-Week Gestational Age Model Provide Valuable Information on Neurodevelopmental Outcomes in Extremely Preterm Infants?
Bibliographic record
Abstract
Our primary objective for this follow-up study was to compare the neurodevelopmental outcomes of a surviving cohort of infants using a split-week gestational model (early versus late) gestational age (GA) and the standard completed GA categorization. Neurodevelopmental outcomes using a split-week GA model defined as early (X, 0–3) and late (X, 4–6), with X being 23–26 weeks GA, were compared to outcomes using completed weeks GA. In total, 1012 infants were included in the study. Statistically significant differences were noted in outcomes between the early and late split of the gestational week at 23 weeks (early vs. late), with 13.3% vs. 54.5% for no neurodevelopmental impairment, and 53.3% vs. 22.7% for significant impairment (p = 0.034), respectively. There were no differences seen in the split week model for 24, 25, and 26 weeks. A trend towards improved neurodevelopmental outcomes was seen with each increasing gestation week. The split-week model did not provide additional information for pregnancies and infants between 24 and 26 weeks gestation. It did, however, provide information for counsel for infants at 23 weeks gestation, showing benefits in the late versus early half of the week.
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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 | 0.005 | 0.022 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".