What Is Normal Growth? Principles, Practicalities and Pitfalls of Growth Assessments in Infants and Children
Bibliographic record
Abstract
BACKGROUND: Growth assessments are a pillar of public health surveillance, individual health screening, and clinical care. Normal growth is defined differently for individuals versus populations. The World Health Organization (WHO) growth standards were developed to describe the pattern of growth in healthy children without socioeconomic limitations whose mothers planned to breastfeed. The growth standards' cut-off points of ±2 standard deviations (z-scores) were defined for population assessments, based on attained size, to describe stunting and wasting at the lower end and overweight at the higher end. In a healthy population, one would expect 2.3% of the population to be above and below these cut-points. Higher child mortality rates associated with higher rates of stunting and wasting noted in observational studies validated these WHO cut-offs. There are knowledge gaps influencing the accuracy and effectiveness of growth assessments in individual children, posing challenges for health care providers. SUMMARY: The principles of assessing normal growth in children and preterm infants are reviewed, along with pitfalls to be avoided. Growth is determined by genetics and modified by the interplay with nutritional, environmental, socioeconomic, and possibly intergenerational factors. This complexity is reflected at both the population and individual level. However, normal growth in an individual has unique-specific factors so requires a comprehensive assessment. Normal growth for an individual child could be defined as the progression of changes in anthropometric measurements to achieve the individual's genetic potential. A misdiagnosis of growth faltering can occur if infants and children are assessed with one-time rather than serial measures, and if age is not corrected for prematurity. Health care provider sensitivity and cognizance when communicating about a child's size is important for parental reassurance and avoiding stigma and unnecessary pressures or restrictions around feeding.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".