BMI-for-Age and Weight-for-Length in Children 0 to 2 Years
Bibliographic record
Abstract
OBJECTIVES: To determine the agreement between weight-for-length and BMI-for-age in children 0 to <2 years by using research-collected data, examine factors that may affect agreement, and determine if agreement differs between research- and routinely collected data. METHODS: Cross-sectional data on healthy, term-born children (n = 1632) aged 0 to <2 years attending the TARGet Kids! practice-based research network in Toronto, Canada (December 2008-October 2014) were collected. Multiple visits for each child were included. Length (cm) and weight (kg) measurements were obtained by trained research assistants during research visits, and by nonresearch staff during all other visits. BMI-for-age z-scores were compared with weight-for-length z-scores (the criterion measure). RESULTS: The correlation between weight-for-length and BMI-for-age was strong (r = 0.986, P < .0001) and Bland-Altman plots revealed good agreement (difference = -0.08, SD = 0.20, P = .91). A small proportion (6.3%) of observations were misclassified and most misclassifications occurred near the percentile cutoffs. There were no differences by age and sex. Agreement was similar between research- and routinely collected data (r = 0.99, P < .001; mean difference -0.84, SD = 0.20, P = .67). CONCLUSIONS: Weight-for-length and BMI-for-age demonstrated high agreement with low misclassification. BMI-for-age may be an appropriate indicator of growth in the first 2 years of life and has the potential to be used from birth to adulthood. Additional investigation is needed to determine if BMI-for-age in children <2 years is associated with future health outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".