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Record W2892485586 · doi:10.1002/9781119266594.ch67

Exercise for Osteoporotic Fracture Prevention and Management

2018· other· en· W2892485586 on OpenAlexaff
Robin M. Daly, Lora Giangregorio

Bibliographic record

VenuePrimer on the metabolic bone diseases and disorders of mineral metabolism · 2018
Typeother
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsResearch Institute for AgingUniversity of Waterloo
Fundersnot available
KeywordsOsteoporosisMedicinePhysical therapyResistance trainingSarcopeniaMuscle massPhysical medicine and rehabilitationMuscle strengthBone massPhysical activityStrength trainingGerontologyInternal medicine

Abstract

fetched live from OpenAlex

This chapter summarizes the evidence with regard to the role of exercise for the prevention and management of osteoporosis, falls, and fractures. It focuses on the role of exercise in middle-aged and older adults, it is important to acknowledge that the growing years represent a critical period during which exercise can enhance the mass, structure, and strength of bone. Clinical practice guidelines recommend exercise as a strategy to reduce the risk of fractures. Progressive resistance training (PRT) is the most effective mode of exercise to improve muscle mass, size, and strength, including in individuals who are frail or have a history of fracture. The inclusion of PRT in a multimodal exercise program may have the advantage of preventing diet-induced bone and muscle loss in individuals on a weight loss program.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.037
Threshold uncertainty score0.125

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0370.013

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 head0.015
GPT teacher head0.294
Teacher spread0.280 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations25
Published2018
Admission routes1
Has abstractyes

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