Preterm growth assessment: the latest findings on age correction
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effect of age correction up to 36 months of age for growth assessments of extremely preterm (<28 weeks) and very preterm (28 to <32 weeks) infants. STUDY DESIGN: This longitudinal analysis used data from the Preterm Infant Multicenter Growth Study (2001-2014). RESULTS: 1,416 children were included (Median gestational age = 27 weeks). Chronological age-based weight, height, and head circumference z-scores were consistently lower than those based on corrected age for all ages (0, 4, 8, 21 and 36 months) by up to -5.2 (95% confidence interval -5.4, -5.1) z-scores for length at term. Using chronological age, higher proportions of children were misclassified as having suboptimal growth (up to 72.9% misdiagnosed as stunted and 89.8% misdiagnosed as underweight at term). CONCLUSION: For extremely and very preterm children, age correction is required for all growth measures through 36 months of corrected age.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".