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Record W4293916951 · doi:10.54097/hset.v11i.1381

The effect of Calcium and Sodium Intake on Bone Health

2022· article· en· W4293916951 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHighlights in Science Engineering and Technology · 2022
Typearticle
Languageen
FieldMedicine
TopicVitamin D Research Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsOsteoporosisBone resorptionBone healthBone mineralEndocrinologyInternal medicineCalciumMedicineBone remodelingBone densityCalcium metabolismVitamin D and neurologyPeak bone massHormonePhysiology

Abstract

fetched live from OpenAlex

Bone health gets more and more attention in the younger population since the peak bone mass will be achieved during one’s childhood and adolescence. Bone mineral density (BMD), an important indicator, is commonly used to indicate overall bone health. The development of BMD is critical during the growth period, which could contribute to less incidence of osteoporosis as people get old. Osteoporosis is one of the most common bone diseases, which could lead to other health complications. In addition to other factors affecting bone health such as physical activity and hormones, nutrition is the most important factor of bone health. Calcium (Ca) and vitamin D (VD) act hand in hand. The absorption of dietary calcium is highly affected by VD. Different hormones regulate Ca homeostasis and balance in the body. Moreover, bone remodeling is tightly regulated to conserve bone integrity. The bone formation is tightly coupled to the resorption. Dietary intake of sodium (Na) cannot be ignored as well. High intake of Na is negatively associated with bone health. The DASH diet with low sodium intake positively affects bone mineral density to some extent.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.407
Threshold uncertainty score0.260

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.282
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it