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Record W2337658275

Bone health: osteoporosis, calcium and vitamin D.

2011· article· en· W2337658275 on OpenAlexaffabout
Didier Garriguet

Bibliographic record

VenuePubMed · 2011
Typearticle
Languageen
FieldMedicine
TopicVitamin D Research Studies
Canadian institutionsStatistics Canada
Fundersnot available
KeywordsOsteoporosisMedicineVitamin D and neurologyLogistic regressionDietary Reference IntakePopulationCalciumVitaminInternal medicineGerontologyEnvironmental healthNutrient
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Osteoporosis is a bone disease that predisposes to fractures. Sufficient intake of calcium and vitamin D is recommended for prevention and treatment. DATA AND METHODS: Based on 28,406 respondents aged 50 or older to the 2009 Canadian Community Health Survey (CCHS)--Healthy Aging, the population who reported being diagnosed with osteoporosis is profiled. Analysis of calcium and vitamin D intake is based on 10,879 respondents aged 50 or older to the 2004 CCHS--Nutrition. Frequencies, averages and cross-tabulations were produced to estimate the prevalence of diagnosed osteoporosis, dietary intake of calcium and vitamin D, the use of supplements, and total calcium and vitamin D intake. Associations between a diagnosis of osteoporosis and socio-economic, dietary and lifestyle factors were examined with multiple logistic regression. RESULTS: In 2009, 19.2% of women and 3.4% of men aged 50 or older reported having been diagnosed with osteoporosis; the 2004 rates were similar. Age, sex and household income were associated with the probability of reporting osteoporosis. In 2004, based on dietary and supplement intake, 45% to 69% of the population aged 50 or older had inadequate intake of calcium, and 54% to 66% had inadequate intake of vitamin D. INTERPRETATION: A large percentage of people aged 50 or older, particularly women, have osteoporosis. The prevalence of inadequate intake of calcium and vitamin D is relatively high.

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.

How this classification was reachedexpand

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.828
Threshold uncertainty score0.344

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.082
GPT teacher head0.303
Teacher spread0.221 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations59
Published2011
Admission routes2
Has abstractyes

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