Calcium status assessment at the population level: Candidate approaches and challenges
Bibliographic record
Abstract
Inadequate dietary calcium intake is a global public health problem that disproportionately affects low- and middle-income countries. However, the calcium status of a population is challenging to measure, and there are no standard methods to identify high-risk communities even in settings with an elevated prevalence of a disease caused or exacerbated by low calcium intake (e.g., rickets). The calcium status of a population depends on numerous factors, including intake of calcium-rich foods; the bioavailability of the types of calcium consumed in foods and supplements; and population characteristics, including age, sex, vitamin D status, and genetic attributes that influence calcium retention and absorption. The aim of this narrative review was to assess candidate indicators of population-level calcium status based on a range of biomarkers and measurement methods, including dietary assessment, calcium balance studies, hormonal factors related to calcium, and health outcomes associated with low calcium status. Several promising approaches were identified, but there was insufficient evidence of the suitability of any single indicator to assess population calcium status. Further research is required to develop and validate specific indicators of calcium status that could be derived from the analysis of data or samples that are feasibly collected in population-based surveys.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".