<scp>B</scp> <scp>MI</scp> ‐for‐age percentile curves for older adults
Bibliographic record
Abstract
Abstract Objective The objective of this manuscript is to present BMI‐for‐age percentile curves for men and women aged 45 to 90 years. Methods Weighted empirical percentile estimates were calculated using data from the Canadian Longitudinal Study on Aging (CLSA) comprehensive cohort (2011–2018) according to age and sex. Statistical smoothing procedures were used to generate smoothed curves for the percentile values. Overweight and obesity were defined as BMI greater than the 85th and 95th percentile for age and sex, respectively. Results In order to create BMI‐for‐age percentile curves, n = 56,705 observations were used ( n = 29,961 individuals at baseline and n = 26,744 individuals at the first follow‐up visit). In men, absolute values for BMI percentiles are lower than those in women, and the decline in BMI begins earlier (i.e., at a younger age). In women, the 95th percentile threshold for BMI is highest between ages 59 and 67 years (i.e., 41 kg/m 2 ), and in men, the 95th percentile threshold for BMI is highest between ages 51 and 62 years (i.e., 39 kg/m 2 ). Conclusions BMI‐for‐age percentile curves demonstrate how an individual's BMI value compares with values from a reference population comprising individuals of the same age and sex. This approach has widespread utility to determine eligibility for interventions and as a tool to incorporate into clinical models of care for obesity management in an aging population.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".