Associated Factors of Cognitive Frailty in People Living with HIV Aged 50 and Older: A Cross-Sectional Study
Bibliographic record
Abstract
INTRODUCTION: Cognitive frailty (CF), which typically precedes dementia and functional decline, serves as a more robust predictor of adverse health outcomes compared to physical frailty alone, representing a critical challenge in promoting healthy aging among older people living with HIV (PLWH) aged ≥ 50 years. This study aimed to investigate the prevalence of cognitive frailty and identify its associated factors among PLWH aged ≥ 50 years. METHODS: A convenience sample of 344 PLWH ≥ 50 years was recruited from a tertiary Grade A hospital in Zunyi, China. Physical frailty: evaluated via the Fatigue, Resistance, Ambulation, Illnesses, and Loss of Weight (FRAIL) Scale; Cognitive function: assessed via the Chinese version of the Montreal Cognitive Assessment (MoCA). Participants were divided into the cognitive frailty group (FRAIL score ≥ 3 and MoCA score < 26), the non-cognitive frailty group. Binary logistic regression analysis was conducted with SPSS 29.0 to identify factors associated with CF. RESULTS: The prevalence of CF among the 344 PLWH aged ≥ 50 years was 37.5%. Regression analysis revealed that the following associated factors (p < 0.05) were independent risk factors for CF in PLWH aged ≥ 50 years: age, education level, weekly frequency of physical activity ≤ 2 sessions, depression, sleep disorders, and EFV-containing regimens. CONCLUSIONS: Cognitive frailty is highly prevalent among PLWH aged ≥ 50 years. Early screening and comprehensive healthcare interventions targeting modifiable risk factors are crucial for delaying or reversing CF progression in this population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".