Predicting cardiometabolic markers in children using tri-ponderal mass index: a cross-sectional study
Bibliographic record
Abstract
OBJECTIVE: ) to predict cardiometabolic risk in a population-based sample of Canadian children and youth. METHODS: We used data from the Canadian Health Measures Survey to model TMI from 6 to 19 years of age. Percentile curves were developed using the LMS method. Logistic regression was used to predict abnormal levels of cardiometabolic markers; predictive accuracy was assessed using the area under the ROC curve (AUC). RESULTS: Mean TMI was relatively stable from ages 6 to 19 years for both sexes, but variability increased with age. There was no notable difference in AUC values for prediction models based on BMI z-score compared with TMI for any of the outcomes. For both BMI z-score and TMI, prediction accuracy was good for homeostasis model assessment insulin resistance and having ≥3 abnormal tests (AUC>0.80), fair for C-reactive protein and poor for the remainder of the outcomes. CONCLUSIONS: The use of a single sex-specific TMI cut-off for overweight or obesity is hampered by the increasing variability of the measure with age. Weight-for-height indices likely have only limited ability to predict cardiometabolic marker levels, and changing the scaling power of height is unlikely to improve predictive accuracy.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".