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Record W4408956883 · doi:10.1093/ageing/afaf064

Fall risk stratification in older adults: low and not-at-risk status still associated with falls and injuries.

2025· article· en· W4408956883 on OpenAlexafffund
Manuel Montero‐Odasso, Frederico Pieruccini‐Faria, Surim Son, Daniela Cristina Carvalho de Abreu, Susan M. Hunter, Jia Liu, M. Brittain Moore, Areej Hezam, Nathalie van der Velde, Tahir Masud, Jesper Ryg, Mirko Petrović, Mark Speechley

Bibliographic record

VenuePubMed · 2025
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsCanadian Physiotherapy AssociationLondon Health Sciences CentreParkwood InstituteWestern University
FundersWeston Family FoundationConsortium canadien en neurodégénérescence associée au vieillissement
KeywordsMedicineFalls in older adultsRisk stratificationInjury preventionRisk assessmentPoison controlOccupational safety and healthEnvironmental healthSuicide preventionGerontologyDemographyInternal medicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND: Falls guidelines recommendations for individuals classified as 'not-at-risk' range from no further actions to offering education and exercises. However, there is a scarcity of prospective studies analysing the rate of falls and injuries in this not-at-risk group to inform recommendations. OBJECTIVE: To prospectively estimate the rate of falls and injuries in older adults considered 'not-at-risk' for falls. DESIGN: Prospective cohort study. SETTING: Geriatric Medicine Clinics. SUBJECTS: Community-dwelling older adults aged 65 and older. METHODS: Falls risk stratification was operationalised by adapting the Centers for Disease Control and Prevention's Stopping Elderly Accidents, Deaths and Injuries algorithm. Associations of risk strata (screened not-at-risk vs. at-risk) with incident falls and injuries were estimated using incidence rate ratios [adjusted incident rate ratio (aIRR), Poisson regression model]. Associations between slow gait speed (<1 m/s) and injurious falls were estimated by risk strata using hazard ratios (adjusted hazard ratio, Cox and Poisson regression model). RESULTS: Of 403 participants, 64% of at-risk individuals fell during the follow-up compared to 41.3% in the not-at-risk group, whilst injurious falls were reported by 63.2% of the not-at-risk group and by 59.7% of the at-risk group. At-risk individuals had a higher rate of falls (aIRR = 3.91, 95% CI: 3.30-4.64, P < .001) but a similar rate of injurious falls as the not-at-risk individuals (aIRR = 1.26, 95% CI: 0.93-1.71; P = .11). Not-at-risk individuals with slow gait speed sustained injurious falls at twice the rate (aIRR = 1.83, 95% CI: 1.12-3.91, P = .008) than those without slow gait speed. CONCLUSIONS: Being screened as not-at-risk for falls does not mean no risk at all. Routinely and universally assessing gait speed could identify not-at-risk individuals who are likely to sustain injuries after a fall and could benefit from primary prevention.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.275
Teacher spread0.266 · 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 designObservational
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

Citations6
Published2025
Admission routes2
Has abstractyes

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