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Record W3216374530 · doi:10.1177/23337214211052398

Low Risk of Fracture With End-Range Movements of the Hip in People With Low Bone Mineral Density: A Narrative Review

2021· review· en· W3216374530 on OpenAlexaff
Christina Ziebart, Caitlin McArthur, Judi Laprade

Bibliographic record

VenueGerontology and Geriatric Medicine · 2021
Typereview
Languageen
FieldMedicine
TopicHip and Femur Fractures
Canadian institutionsUniversity of TorontoDalhousie UniversityWestern University
Fundersnot available
KeywordsOsteoporosisMedicineNarrative reviewPhysical therapyHip fracturePhysical medicine and rehabilitationPsychological interventionBiomechanicsInternal medicineAnatomyIntensive care medicine

Abstract

fetched live from OpenAlex

Background: The risks of end-range movements for people with osteoporosis, specifically at the hips and shoulder, are not well understood. Objectives: To synthesize literature on the safety of stretching for people with osteoporosis by searching: 1) biomechanical literature to determine how much force results from an end-range maneuverer and is required to fracture joint components (focusing on the hip joint) and 2) clinical literature to describe techniques used, populations studied, effects, and reported adverse events. Methods: We conducted two separate search strategies in PubMed, EMBASE, and Scopus (1955–2020). Results: 16 articles described either biomechanical or clinical effects of passive and active end ranges of the hip joint. The largest load in the hip, described in the literature was in a crescent lunge during yoga. The moment produced in a crescent lunge is much smaller than that of the tensile strength of osteoporotic bone, suggesting the crescent lunge movement could be considered safe. Clinically, no adverse events were reported in exercise, stretching or yoga interventions. Conclusion: This review found no evidence that end range movements of the hip are unsafe, but there is little evidence. No studies were identified that explored the risk of humeral fracture during end range stretches.

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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.590
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.301
Teacher spread0.285 · 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 designSystematic review
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

Citations0
Published2021
Admission routes1
Has abstractyes

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