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Record W4294579750 · doi:10.1002/jbm4.10679

Association Among Cognition, Frailty, and Falls and Self‐Reported Incident Fractures: Results From the Canadian Longitudinal Study on Aging ( <scp>CLSA</scp> )

2022· article· en· W4294579750 on OpenAlexafffundabout
Ahreum Lee, Caitlin McArthur, George Ioannidis, Jonathan D. Adachi, Lauren E. Griffith, Lehana Thabane, Lora Giangregorio, Suzanne N. Morin, William D. Leslie, Justin Lee, Αλεξάνδρα Παπαϊωάννου

Bibliographic record

VenueJBMR Plus · 2022
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsUniversity of ManitobaResearch Institute for AgingUniversity of WaterlooPopulation Health Research InstituteMcMaster UniversityDalhousie UniversityHamilton Health SciencesMcGill UniversityImpact
FundersInstitute of AgingCanadian Institutes of Health ResearchCanada Foundation for InnovationGovernment of Canada
KeywordsCognitionMedicineLogistic regressionLongitudinal studyCohortFalls in older adultsRecallProspective cohort studyPoison controlDemographyInjury preventionGerontologyInternal medicineClinical psychologyPsychologyPsychiatryEmergency medicinePathology

Abstract

fetched live from OpenAlex

ABSTRACT Cognition, frailty, and falls have been examined independently as potential correlates of fracture risk, but not simultaneously. Our objective was to explore the association between cognition, frailty, and falls and self‐reported incident fractures to determine if these factors show significant independent associations or interactions. We included participants who completed the Canadian Longitudinal Study on Aging (CLSA) 2012–2015 baseline comprehensive assessment, did not experience any self‐reported fractures in the year prior to cohort recruitment, and completed the follow‐up questionnaire at year 3 ( n = 26,982). We compared all baseline cognitive measures available in the CLSA, the Rockwood Frailty Index (FI), and presence of self‐reported falls in the past 12 months in those with versus without self‐reported incident fractures in year 3 of follow‐up. We used multivariable logistic regression adjusted for covariates and examined two‐way interactions between cognition, frailty, and prior falls. CLSA specified analytic weights were applied. The mean ± standard error (SE) age of participants was 59.5 ± 0.1 years and 52.2% were female. A total of 715 participants (2.7%) self‐reported incident fractures at 3‐year follow‐up. Participants who experienced incident fractures had similar baseline cognition scores (mean ± SE; Rey Auditory Verbal Learning Test [RAVLT]: Immediate recall 6.1 ± 0.1 versus 5.9 ± 0.0; standardized difference [d] 0.124); higher FI scores (mean ± SE; FI 0.134 ± 0.005 versus 0.116 ± 0.001; d 0.193), and a greater percentage had fallen in the past 12 months (weighted n [%] 518 [7.2] versus 919 [3.5]; d 0.165). FI (each increment of 0.08) was associated with a significantly increased risk of self‐reported incident fractures in participants of all ages and those aged 65 years or older (adjusted odd ratio [OR] 1.24, 95% confidence limit [CL] 1.10–1.40; adjusted OR 1.44, 95% CL 1.11–1.52, respectively). The adjusted odds for self‐reported incident fractures in participants of all ages was also significantly associated with falls in the past 12 months prior to baseline (adjusted OR 1.83; 95% CL 1.13–2.97), but not in those aged 65 years or older. No interactions between cognition, frailty, and prior falls were found. However, considering the relatively young age of our cohort, it may be appropriate to make strong inferences in individuals older than 65 years of age. © 2022 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.099
Threshold uncertainty score0.199

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.038
GPT teacher head0.305
Teacher spread0.267 · 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
Published2022
Admission routes3
Has abstractyes

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