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An Evaluation of Select Physical Activity Exercise Classes (PEX) on Bone Mineral Density

2016· article· en· W1838913959 on OpenAlexaff
Tori M. Stone, John C. Young, James W. Navalta, Jonathan E. Wingo

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2016
Typearticle
Languageen
FieldMedicine
TopicBody Composition Measurement Techniques
Canadian institutionsCanadian Society for Exercise Physiology
Fundersnot available
KeywordsBone mineralMineralPhysical activityChemistryMedicinePhysical therapyInternal medicineOsteoporosisOrganic chemistry

Abstract

fetched live from OpenAlex

PURPOSE: To assess the efficacy of structured physical activity classes on bone mineral density (BMD). METHODS: Thirty-nine women ages 18–35 y who were either enrolled in a yoga class (n=14), cardio-kickboxing class (n=13), or no physical activity class (control; n=12) voluntarily consented to participate. Dual-energy x-ray absorptiometry (DEXA) scans of the hip, spine, and total body were measured just before commencement and just after completion of the semester-long classes. Likewise, blood samples were drawn pre- and post-semester for measurement of osteocalcin, and dietary and physical activity questionnaires were also completed. RESULTS: Neither yoga nor cardio-kickboxing affected BMD at any of the measured sites. Osteocalcin concentration increased from pre- to post-semester measures (pre = 12.15 ng/mL, post = 41.15 ng/mL; P < 0.001), but groups were not different (P = 0.314). CONCLUSIONS: Based on these data, 12 weeks of yoga and cardio-kickboxing physical activity classes were insufficient to induce bone mineral density changes. However, osteoblast activity was likely elevated as reflected by increased blood osteocalcin concentrations over time, thereby indicating stimulation of the bone formation process.

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)0.005
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 score0.669
Threshold uncertainty score0.725

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.043
GPT teacher head0.350
Teacher spread0.308 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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