Growth of preterm infants at the time of global obesity
Bibliographic record
Abstract
Preterm birth, 90% of which occurs between 32 and <37 weeks' gestation,1 2 is a complex heterogeneous syndrome interlinked with the stillbirth and intrauterine growth restriction syndromes.3 4 Its phenotypes are associated with different gains in neonatal weight,5 morbidity and mortality,6 and perhaps body composition, growth and development. Preterm birth is related to several aetiologies, although nearly 30% of all preterm births are not associated with any maternal/pregnancy conditions or fetal growth restriction.6 This group is, therefore, the target population for constructing postnatal growth standards for preterm infants.7 8 There is disagreement, however, about how best to monitor the postnatal growth of such a heterogeneous group of newborns. In fact, a systematic review identified 61 existing longitudinal charts for preterm infants, many with considerable limitations in gestational age estimation, body measurement, length of follow-up and description of feeding practices and morbidities.9 The problem requires four fundamental issues to be considered.10 First , size at birth measures (eg, birth weight, length and head circumference), which are taken only once per infant, are a retrospective summary of fetal growth reflecting the intrauterine environment and overall efficiency of placental nutrient transfer. Postnatal growth, on the other hand, requires repeated anthropometric measures after birth, complemented by feeding practices and morbidity data. Therefore, the use of size at birth by gestational age, cross-sectional data taken only at birth to evaluate the postnatal growth of preterm infants cannot be justified either physiologically or clinically. Implicit in the concept of growth is the requirement for repeated measures over time, which can obviously not be captured with a single birth measure. Furthermore, fundamental factors determining the postnatal growth of preterm infants that change over time, such as feeding regimens and organ maturity influencing morbidity, are by definition not included in the …
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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 | 0.001 | 0.010 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".