Postnatal growth failure in preterm infants: ascertainment and relation to long-term outcome
Bibliographic record
Abstract
OBJECTIVE: Traditional measure of postnatal growth failure assessment has poor discriminatory power for long-term outcomes. Our objective was to identify measure of postnatal growth failure associated with long-term outcome in preterm infants born at < 28 weeks' gestation. PATIENTS AND METHODS: Four measures of defining postnatal growth failure at 36 weeks corrected gestational age: (1) weight < 10(th) centile, (2) weight < 3(rd) centile, (3) z score difference from birth > 1 and, (4) z score difference from birth > 2; were compared for their predictive values and strength of association with adverse neurodevelopmental outcomes at 18-24 months. RESULTS: Postnatal growth failure defined as a decrease in z score of > 2 between birth and 36 weeks corrected gestational age had the best predictive values compared to other postnatal growth failure measures, however, it was significantly associated with psychomotor developmental (P=0.006) but not with mental developmental indices (P=0.379). CONCLUSION: Postnatal growth failure defined by z score change influenced psychomotor but not mental tasks in this cohort. This method of ascertainment could be useful to identify infants who might benefit from nutritional 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 | 0.003 | 0.017 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".