Growth Assessment in Infants and Toddlers Using Three Different Reference Charts
Bibliographic record
Abstract
OBJECTIVE: To determine if the proportion of children < or =24 months old in a tertiary care facility defined as at risk of undernutrition or overnutrition differs according to different references used for assessment: the Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS) or Tanner-Whitehouse (Tanner) growth charts for weight-for-age and length-for-age. METHODS: Lengths and weights were measured on infants (207 female, 341 male) aged < or =24 months admitted from or attending clinics in the General Pediatric or Respiratory Medicine Programs at The Hospital for Sick Children, Toronto. Weight-for-age and length-for-age percentiles and percent ideal body weight were electronically computed. RESULTS: The proportion of all children whose weight-for-age was <3rd percentile (at risk of undernutrition) was greatest using the CDC growth charts (22.5%) compared with the NCHS (15.9%) or Tanner (19.2%) growth charts. Likewise, the proportion of all infants/toddlers with percent ideal body weight <90 (at risk of undernutrition) was greatest using the CDC (32.3%) compared with the NCHS (22.1%) or Tanner (25.9%) growth charts. In contrast, the percentage of children whose percent ideal body weight was > or =110% (at risk of overnutrition) was least using the CDC (18.1%) compared with the NCHS (26.1%) or Tanner (22.4%) growth charts. CONCLUSION: More children aged < or =24 months will be defined as at risk of undernutrition and fewer at risk of overnutrition when using weight-for-age or percent ideal body weight and the CDC growth charts compared with the NCHS or Tanner growth charts. As a result, requests for a more detailed nutritional assessment for undernutrition will likely follow implementation of the CDC growth charts in a tertiary care setting. As the CDC, NCHS and Tanner growth charts are growth "references" rather than "standards," other than for screening purposes, they should not be used in isolation when assessing growth and nutritional status.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".