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Effect of physical activity on bone mineral density assessed by limb dominance across the lifespan

2000· article· en· W2075898553 on OpenAlexaff
Philip D. Chilibeck, Kelly Davison, D. G. Sale, Colin E. Webber, Robert A. Faulkner

Bibliographic record

VenueAmerican Journal of Human Biology · 2000
Typearticle
Languageen
FieldMedicine
TopicBody Composition Measurement Techniques
Canadian institutionsMcMaster UniversityUniversity of Saskatchewan
Fundersnot available
KeywordsBone mineralHumDominance (genetics)Bone densityMedicineSignificant differenceAge groupsEnergy expenditureDemographyInternal medicineBiologyOsteoporosis

Abstract

fetched live from OpenAlex

Bone mineral density is higher in dominant vs. nondominant limbs, implying that the greater use of dominant limbs in everyday activities results in the deposition of more bone or that the dominant limb is genetically larger. The objective of the present study was to determine whether bone mineral density differences between dominant and nondominant arms were greater in older vs. younger women. To determine whether this was due to a greater lifetime of preferential loading of the dominant arm, differences between dominant and nondominant arms were compared to accumulated amounts of physical activities which emphasized use of the dominant arm. Bone mineral density of dominant and nondominant arms was assessed by dual-energy X-ray absorptiometry in groups of younger (n = 35; age = 20.9) and older (n = 53; age = 57.4) women. The difference between arms was greater in the older vs. the younger group (5.2% vs. 1.9%, respectively, P < 0.01). Within the older group, total lifetime energy expenditure during activities emphasizing loading of the dominant arm correlated with the bone mineral difference between dominant and nondominant arms (r = 0.47, P < 0.01). This implies that a greater lifetime of preferential loading of the dominant arm in the older group resulted in a greater difference between arms. Am. J. Hum. Biol. 12:633-637, 2000. Copyright 2000 Wiley-Liss, Inc.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.012
GPT teacher head0.347
Teacher spread0.335 · 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 designBench or experimental
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

Citations19
Published2000
Admission routes1
Has abstractyes

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