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Record W2995358626 · doi:10.1093/ageing/afz164.31

31 Evidence, Assumptions, and Emerging Treatments for Falls Prevention

2019· article· en· W2995358626 on OpenAlexaff
Manuel Montero‐Odasso

Bibliographic record

VenueAge and Ageing · 2019
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsWestern University
Fundersnot available
KeywordsCognitionMedicineFalls in older adultsGaitPoison controlInjury preventionPhysical medicine and rehabilitationFall preventionHuman factors and ergonomicsCognitive declineObservational studyPopulationGerontologyPsychiatryDementiaDiseaseMedical emergencyPathology

Abstract

fetched live from OpenAlex

Abstract Falls is a common geriatric syndrome that increases morbidity and mortality. Much of our understanding of falls mechanisms derives from studies that excluded or did not evaluate cognitively impaired older adults. This has limited the evidence for managing falls in this population and generated gaps in our understanding of how cognitive processes affect the pathophysiology of falls. This presentation will provide an overview of the role of cognition in falls with potential implications for managing and preventing falls in older adults. A thorough review of observational and interventional studies addressing the role of cognition on falls will be appraised. The importance of the gait-cognition relationship in aging and neurodegeneration is revised to highlight the role of brain motor control deficits in fall risk. The benefits of dual-task gait assessments as a marker of fall risk is reviewed. Therapeutic approaches for reducing falls by improving certain aspects of cognition will be also appraised.

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)0.009
metaresearch head score (Gemma)0.030
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: Review · Consensus signal: Review
Teacher disagreement score0.014
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.030
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.003
Science and technology studies0.0010.002
Scholarly communication0.0040.004
Open science0.0020.002
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0140.003

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 head0.033
GPT teacher head0.322
Teacher spread0.289 · 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
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

Citations1
Published2019
Admission routes1
Has abstractyes

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