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Record W2045575986 · doi:10.1080/078538902321012388

The roles of adulthood behavioural factors and familial influences in bone density among men

2002· article· en· W2045575986 on OpenAlexaff
Tapio Videman, Michele C. Battié, Laura E. Gibbons, Esko Vanninen, Jaakko Kaprio, Markku Koskenvuo

Bibliographic record

VenueAnnals of Medicine · 2002
Typearticle
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsUniversity of Alberta
FundersNational Institute of Arthritis and Musculoskeletal and Skin Diseases
KeywordsFemoral neckMedicineOsteoporosisBone mineralBone densityPhysical therapyLumbar spinePhysical activityLumbarCalciumCalcium supplementationInternal medicinePhysiologySurgery

Abstract

fetched live from OpenAlex

BACKGROUND: A primary strategy in osteoporosis prevention is advice on exercise, smoking, and calcium intake, although its practical value is unclear. AIM: To investigate the roles of such factors on bone density (BMD) after considering the influences of familial aggregation (combined effects of genetics and familial influences) in Finnish men 35-69 years old. METHODS: We selected 105 male monozygotic twin pairs, with discordance in suspected determinants. RESULTS: Dietary calcium was associated with BMD of the femoral neck; and body weight and lifetime frequency of endurance and ball game sport activities were associated with both femoral neck and lumbar BMD. Occupational loading and smoking were associated with neither. However, age and familial aggregation explained 73% of the variance of BMD in both the femoral neck and lumbar spine; calcium intake explained 1% in femoral neck and lifetime exercise 1% in lumbar spine. CONCLUSIONS: The effects of dietary calcium and physical activity that are not 'embedded' in the familial influences had very modest effects on the variance of BMD. Thus our chances of influencing BMD in later adulthood by targeting behavioural habits are likely to be limited. Interventions focused on childhood and the family unit may achieve more beneficial long-term results.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.087
GPT teacher head0.364
Teacher spread0.277 · 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

Citations15
Published2002
Admission routes1
Has abstractyes

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