MétaCan
Menu
Back to cohort
Record W2056571611 · doi:10.1097/bor.0b013e3283570238

Mechanical stimuli and bone health

2012· review· en· W2056571611 on OpenAlexaff
Angela M. Cheung, Lora Giangregorio

Bibliographic record

VenueCurrent Opinion in Rheumatology · 2012
Typereview
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMedicineOsteoporosisBone mineralObservational studyPhysical therapyBone healthPsychological interventionSclerostinClinical trialBone densityFragility fracturePhysical medicine and rehabilitationInternal medicineNursing

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: With the recent emergence of associations of bisphosphonate therapy with atypical fractures and osteonecrosis of the jaw, there is renewed interest among clinicians and patients for nonpharmacological approaches to bone health. Here, we review the new studies published in the past year or two that advance our knowledge of the effect of mechanical stimuli on bone health. RECENT FINDINGS: Physical activity is associated with serum sclerostin levels; the most physically active individuals have the lowest serum sclerostin levels. Observational trials suggest that physical activity participation results in higher bone mass, but clinical trials suggest that the effects of exercise on areal bone mineral density are small, and vary with the site measured and the type of exercise. Based on current data, it may be best to combine progressive resistance training with interventions such as walking or aerobic dancing if the desire is to improve both spine and hip in postmenopausal women. Low-magnitude high-frequency whole body vibration does not improve bone mineral density and bone structure in postmenopausal women. SUMMARY: Physical activity and exercise are important for the maintenance of musculoskeletal health as we age. Future studies need to investigate the effects of exercise in older populations with rheumatological diseases and those with a history of fragility fractures.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.933
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.260
GPT teacher head0.506
Teacher spread0.246 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations57
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueCurrent Opinion in RheumatologySame topicBone health and osteoporosis researchFrench-language works237,207