Monitoring human growth and development: a continuum from the womb to the classroom
Bibliographic record
Abstract
A comprehensive set of fully integrated anthropometric measures is needed to evaluate human growth from conception to infancy so that consistent judgments can be made about the appropriateness of fetal and infant growth. At present, there are 2 barriers to this strategy. First, descriptive reference charts, which are derived from local, unselected samples with inadequate methods and poor characterization of their putatively healthy populations, commonly are used rather than prescriptive standards. The use of prescriptive standards is justified by the extensive biologic, genetic, and epidemiologic evidence that skeletal growth is similar from conception to childhood across geographic populations, when health, nutrition, environmental, and health care needs are met. Second, clinicians currently screen fetuses, newborn infants, and infants at all levels of care with a wide range of charts and cutoff points, often with limited appreciation of the underlying population or quality of the study that generated the charts. Adding to the confusion, infants are evaluated after birth with a single prescriptive tool: the World Health Organization Child Growth Standards, which were derived from healthy, breastfed newborn infants, infants, and young children from populations that have been exposed to few growth-restricting factors. The International Fetal and Newborn Growth Consortium for the 21st Century Project addressed these issues by providing international standards for gestational age estimation, first-trimester fetal size, fetal growth, newborn size for gestational age, and postnatal growth of preterm infants, all of which complement the World Health Organization Child Growth Standards conceptually, methodologically, and analytically. Hence, growth and development can now, for the first time, be monitored globally across the vital first 1000 days and all the way to 5 years of age. It is clear that an integrative approach to monitoring growth and development from pregnancy to school age is desirable, scientifically supported, and likely to improve care, referral patterns, and reporting systems. Such integration can be achieved only through the use of international growth standards, especially in increasingly diverse, mixed ancestry populations. Resistance to new scientific developments has been hugely problematic in medicine; however, we are confident that the obstetric and neonatal communities will join their pediatric colleagues worldwide in the adoption of this integrative strategy.
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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 | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".