Effect of Correcting the Postnatal Age of Preterm-Born Children on Measures of Associations Between Infant Length-for-Age <i>z</i> Scores and Mid-Childhood Outcomes
Bibliographic record
Abstract
Child growth standards are commonly used to derive age- and sex-standardized anthropometric indices but are often inappropriately applied to preterm-born children (<37 weeks of gestational age (GA)) in epidemiology studies. Using the 2004 Pelotas Birth Cohort, we examined the impact of correcting for GA in the application of child growth standards on the magnitude and direction of associations in 2 a priori-selected exposure-outcome scenarios: infant length-for-age z score (LAZ) and mid-childhood body mass index (scenario A), and infant LAZ and mid-childhood intelligence quotient (scenario B). GA was a confounder that had a strong (scenario A) or weak (scenario B) association with the outcome. Compared with uncorrected postnatal age, using GA-corrected postnatal age attenuated the magnitude of associations, particularly in early infancy, and changed inferences for associations at birth. Although differences in the magnitude of associations were small when GA was weakly associated with the outcome, model fit was meaningfully improved using corrected postnatal age. When estimating population-averaged associations with early childhood growth in studies where preterm- and term-born children are included, incorporating heterogeneity in GA at birth in the age scale used to standardize anthropometric indices postnatally provides a useful strategy to reduce standardization errors.
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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 | 0.036 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".