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Record W2731994971 · doi:10.1093/geroni/igx004.074

GAIT REHABILITATION PROGRAMS AND AGING: NEW ADVANCES FROM THE CANADIAN GAIT CONSORTIUM

2017· article· en· W2731994971 on OpenAlexaffabout
Olivier Beauchet, Louis Bherer, John M. Barden

Bibliographic record

VenueInnovation in Aging · 2017
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsUniversity of ReginaUniversité de MontréalMcGill University
Fundersnot available
KeywordsGaitPhysical medicine and rehabilitationRehabilitationPsychologyMedicinePhysical therapy

Abstract

fetched live from OpenAlex

This symposium will present recent advances in gait rehabilitation (GR) programs in older adults. Gait is the medical term use to describe human bipedal locomotion. Gait impairment leads to unsafe gait and several adverse consequences such as falls. GR is the act of restoring safe gait. It is a key component of preventive, symptomatic and curative interventions of gait impairment. Physical activity and exercises are the main component of GR. Several trials have suggested that physical exercises also protect against cognitive decline. But the question of the best outcome to determine the GR effects remains to determine. Recent advances in the understanding of mechanisms of age-related gait impairment, like interaction between gait and cognition or the key role of vitamin D deficiency, raise the development of innovative and creative GR programs not only based on physical activity. An improvement of physical performance and fall reduction have been reported with vitamin D supplementation. It has also been reported that motor imagery, defined as mentally simulating a given action without actual execution, combined with physical exercises resulted in a more significant improvement of motor performance than physical exercises alone. New GR programs based on merging mental tasks, physical exercises and vitamin D supplementation are developed. In addition, new technology like software on electronic devices playing the role of virtual coach or corresponding to interactive video games has now been used to promote physical activity and change exercise behavior.

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)0.011
metaresearch head score (Gemma)0.009
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: Empirical · Consensus signal: none
Teacher disagreement score0.290
Threshold uncertainty score0.584

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.008
Science and technology studies0.0030.003
Scholarly communication0.0040.002
Open science0.0020.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0140.001

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 head0.026
GPT teacher head0.314
Teacher spread0.288 · 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
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

Citations0
Published2017
Admission routes2
Has abstractyes

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