SELF-REPORTED CALCIUM INTAKE AND BONE MINERAL CONTENT OF CHILDREN
Bibliographic record
Abstract
The primary purpose of this study was to investigate the relationship between self -reported calcium (Ca) intake and bone mineral content (BMC) in children. Maximizing BMC during childhood may reduce the risk of osteoporosis later in life. Unfortunately, the validity of self -reported dietary intake may be adversely affected by underreporting of energy intake (EI). The first objective of this study was to investigate the problem of underreporting of EI in a sample of children by calculating a ratio of reported EI to estimated basal metabolic rate (EI:BMRest ) for each subject. The second objective was to determine whether self -reported Ca intake was an independent predictor of BMC or annual change in BMC (ABMC) in a sample of children. Subjects, aged 8 to 17 years, were participants in the University of Saskatchewan Pediatric Bone Mineral Accrual Study. Underreporting was present, with approximately 25% of subjects having EI:BMRest < 1. Children with higher ponderal indices (PI, kg/m3) were more likely to be underreporters. Among females, age was positively associated with odds of underreporting, but this age relationship was not seen in males. Because of the confounding effect of underreporting, it was hypothesized that an energy-adjusted expression of Ca intake such as calcium density (CAD) would be a better predictor of bone mass than unadjusted Ca intake. Multiple linear regression revealed unadjusted Ca intake to be a predictor of BMC in males in the total body (p = 0.08) and in the lumbar spine (p = 0.03) . CAD was not necessary to correct for underreporting in males, but this was likely due to the nature of the multivariate model used. Neither unadjusted Ca nor CAD was a predictor of BMC in females; therefore, if a relationship between Ca and BMC in females was being obscured by underreporting, CAD could not correct for this. The failure to see any relationship in females was likely a result of the different nature of underreporting in this group compared to males. No relationship was seen between Ca or CAD and BMC but possible reasons for this were insufficient power and/or variable maturational stages. If the higher BMC associated with high Ca intake observed in males is sustained until older adulthood, it would provide some protection against osteoporosis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".