Linking cardiorespiratory fitness classification criteria to early subclinical atherosclerosis in children
Bibliographic record
Abstract
It is unclear if cardiorespiratory fitness (CRF) can be used as a screening tool for premature changes in carotid intima-media thickness (cIMT) in paediatric populations. The purpose of this cross-sectional study was 3-fold: (i) to determine if CRF can be used to screen increased cIMT; (ii) to determine an optimal CRF cut-off to predict increased cIMT; and (iii) to evaluate its ability to predict increased cIMT among children in comparison with existent CRF cut-offs. cIMT was assessed with high-resolution ultrasonography and CRF was determined using a maximal cycle test. Receiver operating characteristic analyses were conducted in boys (n = 211) and girls (n = 202) aged 11-12 years to define the optimal sex-specific CRF cut-off to classify increased cIMT (≥75th percentile). Logistic regression was used to examine the association between the CRF cut-offs with the risk of having an increased cIMT. The optimal CRF cut-offs to predict increased cIMT were 45.81 and 34.46 mL·kg(-1)·min(-1) for boys and girls, respectively. The odds-ratios for having increased cIMT among children who were unfit was up to 2.8 times the odds among those who were fit (95% confidence interval: 1.40-5.53). Considering current CRF cut-offs, only those suggested by Adegboye et al. 2011. (Br. J. Sports Med. 45(9): 722-728) and Boddy et al. 2012 (PLoS One, 7(9): e45755) were significant in predicting increased cIMT. In conclusion, CRF cut-offs (boys: ≤ 45.8; girls: ≤ 34.5 mL·kg(-1)·min(-1)) are associated with thickening of the arterial wall in 11- to 12-year-old children. Low CRF is an important cardiovascular risk factor in children and our data highlight the importance of obtaining an adequate CRF.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".