Effects of physical activity, body fat, and salivary cortisol on mucosal immunity in children
Bibliographic record
Abstract
This study examined relationships among physical activity, body composition, and stress- and immunity-related variables in fifth grade children (10-11 yr) in Southern Ontario. The 29 boys and 32 girls, who participated in the study, performed a 20-m shuttle run for prediction of aerobic fitness. Bioelectrical impedance was used to assess relative body fat. Standardized questionnaires were used to determine physical activity-related variables and frequency of upper respiratory tract infection (URTI). Resting saliva samples were collected and tested for resting cortisol and resting secretory immunoglobulin A (SIgA). Subjects wore a pedometer for 48 h to estimate their average total distance traveled per day. SIgA was significantly correlated with reported URTIs but was not related to salivary cortisol, physical activity, fitness level, or relative body fat. Children who spent more time in sport activities and had higher aerobic fitness reported fewer "sick" days. Children with body fat higher than 25% reported significantly (P < 0.05) more sick days than the rest of the cohort. There were no gender differences in SIgA, URTI frequency, and cortisol levels. The test-retest reproducibility for salivary cortisol was 0.66 (P < 0.01), whereas long-term SIgA reproducibility was nonsignificant for repeated measurements taken after 6 wk. Resting secretory immunity was not strongly related to fitness and physical activity, but there was evidence that reduced physical activity and excess body fat can result in higher URTI incidence.
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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 | 0.000 | 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) | 0.001 | 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".