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Record W4414018974 · doi:10.1177/07334648251360096

The Worse the Physical Function, the More Probable the Concerns about Falling

2025· article· en· W4414018974 on OpenAlexaff
Xiaoxi Hu, Xiaoru Sun, Xiaoyan Jiang, Hui Zhang, Xiaofeng Wang

Bibliographic record

VenueJournal of Applied Gerontology · 2025
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsInstitute of Aging
FundersShanghai Municipal Health Commission
KeywordsRisk stratificationFalling (accident)MedicinePsychological interventionPhysical activityGerontologyRisk assessmentDemographyPhysical therapyInternal medicineEnvironmental healthComputer science

Abstract

fetched live from OpenAlex

Few studies have explored the impact of poor physical function on concerns about falling (CaF) in older adults, often with low sensitivity due to fragmented assessments. The functional continuum (FC), which covers the full range from robust function to disability, may better capture the relationship between physical function and CaF risk. Using data from NHATS (Rounds 1-10), a proportional hazards model examined CaF risk stratification by FC categories. Among 6,547 participants, 30.1% reported CaF. CaF risk increased progressively with worsening FC, showing a significant dose-response relationship (HR 1.13, 95% CI: 1.10-1.16). While FC categories 2-3 showed no significant difference from category 1, risk increased at category 4 (HR 1.35, 95% CI: 1.11-1.65) and doubled at categories 6-8. CaF risk increases with worsening physical function. FC can complement existing risk tools to identify high-risk older populations and guide the design of targeted interventions to prevent CaF.

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)0.006
metaresearch head score (Gemma)0.035
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 score0.011
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.035
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.033
GPT teacher head0.373
Teacher spread0.340 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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