Patterns and predictive factors of loss of the independence trajectory among community-dwelling older adults
Bibliographic record
Abstract
BACKGROUND: Independence is related to the aging process. Loss of independence is defined as the inability to make decisions and participate in activities of daily living (ADLs). Independence is related to physical, psychological, biological, and socioeconomic factors. An enhanced understanding of older people's independence trajectories and associated risk factors would enable the develop early intervention strategies. METHODS: Independence trajectory analysis was performed on patients identified in the Unité de Prévention de Suivi et d'Analyse du Vieillissement (UPSAV) database. UPSAV cohort is a prospective observational study. Participants were 221 community-dwelling persons aged ≥75 years followed for 24 months between July 2011-November 2013 and benefits from a prevention strategy. Data were collected prospectively using a questionnaire. Independence was assessed using the "Functional Autonomy Measurement System (Système de Mesure de l'Autonomie Fonctionnelle (SMAF))". Group-based trajectory modeling (GBTM) was performed to identify independence trajectories, and the results were compared with those of k-means and hierarchical ascending classifications. A multinomial logistic regression was performed to identify predictive factors of the independence trajectory. RESULTS: Three distinct trajectories of independence were identified including a "Stable functional autonomy (SFA) trajectory" (53% of patients), a "Stable then decline functional autonomy decline (SDFA) trajectory" (33% of patients) and a "Constantly functional autonomy decline (CFAD) trajectory" (14% of patients). Not being a member of an association, and previous fall were significantly associated of a SDFA trajectory (P < 0.01). Absence of financial and human assistance, no hobbies, and cognitive disorder were significantly associated with a CFAD trajectory (P < 0.01). Previous occupation and multiple pathologies were predictive factors of both declining trajectories SDFA and CFAD. CONCLUSIONS: Community-living older persons exhibit distinct independence trajectories and the predictive factors. The evidence from this study suggests that the prevention and screening for the loss of independence of the older adults should be anticipated to maintaining autonomy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".