Differences in heart rate variability between Asian and Caucasian children living in the same Canadian community
Bibliographic record
Abstract
Heart rate variability (HRV) is an umbrella term for a variety of measures that assess autonomic influence on the heart. Reduced beat-to-beat variability is found in individuals with a variety of cardiac abnormalities. A reduced HRV positively correlates with obesity, poor aerobic fitness, and increasing age. Racial (black-white) differences are apparent in adults and adolescents. We aimed to evaluate (i) Asian-Caucasian differences in HRV and (ii) differences in HRV between girls and boys. Sixty-two children (30 male (15 Caucasian, 15 Asian) and 32 female (15 Caucasian, 17 Asians)) with a mean age of 10.3 +/- 0.6 y underwent 5 min resting HRV recording, fitness testing (Leger's 20 m shuttle), and self-assessed maturity. Outcome HRV measures were a ratio of low to high frequency power (LF:HF), standard deviation of R-R intervals (SDRR) and root mean square of successive R-R intervals (RMSSD). Data were compared between groups using analysis of covariance (ANCOVA). There were no race or sex differences for time domain variables, mean R-R, body mass index, or blood pressure. Compared with Caucasian children, Asian children displayed a higher adjusted (fitness, R-R interval) LF:HF ratio (72.9 +/- 59.4 vs. 120.6 +/- 85.3, p < 0.05). Girls demonstrated a higher adjusted LF:HF power than boys (117.2 +/- 85.1 vs. 76.6 +/- 62.4, p = < 0.05). In conclusion, Asian and Caucasian children display different frequency domain components of heart rate variability.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".