Fear of falling, but not gait impairment, predicts subjective memory complaints in cognitively intact older adults
Bibliographic record
Abstract
AIM: Understanding the risk factors for developing subjective memory complaints (SMC) could help with early screening and treatment for cognitive impairment. The aim of the present study was to explore the risk factors for developing SMC, by focusing on gait-related variables. METHODS: A total of 406 community-dwelling older adults aged 65-85 years without impending cognitive impairment participated in baseline and 1-year follow-up evaluations. A comprehensive evaluation was carried out, and included gait speed and fear of falling (FoF) assessments, and the Montreal Cognitive Assessment test. Logistic regression analyses were carried out to independently evaluate the risk factors at baseline and follow-up evaluations. RESULTS: At baseline, 45.1% of older adults had SMC. The presence of SMC at baseline was associated with being female, subjective hearing loss and FoF. Of 223 participants who did not report SMC at baseline, 48 had newly developed SMC at follow up (21.5%). The significant predictors for developing SMC were being female and FoF, but not gait speed, and were independent of depression symptoms. The Montreal Cognitive Assessment total score at baseline was a marginally significant predictor for developing SMC at follow up (P = 0.06), but a lower score in the language domain was a significant predictor in further analysis. CONCLUSIONS: FoF was a significant risk for future development of SMC, suggesting that FoF might reflect the risk of cognitive impairment at an earlier stage, or that FoF and SMC could share the same basis of anxiety for daily activities. The mechanisms and consequence of this longitudinal relationship require further study. Geriatr Gerontol Int 2017; 17: 1125-1131.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".