A systematic review and meta-analysis to revise the Fenton growth chart for preterm infants
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to revise the 2003 Fenton Preterm Growth Chart, specifically to: a) harmonize the preterm growth chart with the new World Health Organization (WHO) Growth Standard, b) smooth the data between the preterm and WHO estimates, informed by the Preterm Multicentre Growth (PreM Growth) study while maintaining data integrity from 22 to 36 and at 50 weeks, and to c) re-scale the chart x-axis to actual age (rather than completed weeks) to support growth monitoring. METHODS: Systematic review, meta-analysis, and growth chart development. We systematically searched published and unpublished literature to find population-based preterm size at birth measurement (weight, length, and/or head circumference) references, from developed countries with: Corrected gestational ages through infant assessment and/or statistical correction; Data percentiles as low as 24 weeks gestational age or lower; Sample with greater than 500 infants less than 30 weeks. Growth curves for males and females were produced using cubic splines to 50 weeks post menstrual age. LMS parameters (skew, median, and standard deviation) were calculated. RESULTS: Six large population-based surveys of size at preterm birth representing 3,986,456 births (34,639 births < 30 weeks) from countries Germany, United States, Italy, Australia, Scotland, and Canada were combined in meta-analyses. Smooth growth chart curves were developed, while ensuring close agreement with the data between 24 and 36 weeks and at 50 weeks. CONCLUSIONS: The revised sex-specific actual-age growth charts are based on the recommended growth goal for preterm infants, the fetus, followed by the term infant. These preterm growth charts, with the disjunction between these datasets smoothing informed by the international PreM Growth study, may support an improved transition of preterm infant growth monitoring to the WHO growth charts.
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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 | 0.050 | 0.136 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.022 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".