Correlates of frailty phenotype and frailty index and their associations with clinical outcomes
Bibliographic record
Abstract
OBJECTIVES: Frailty is a predictor of adverse health outcomes and can be measured across the life course, including among people living with HIV. The purpose of this study was to examine two commonly used measures of frailty - the frailty index (FI) and frailty phenotype - to assess common characteristics and to describe associations with multimorbidity, falls, and disability in people aging with HIV. METHODS: This was a cross-sectional observational study including 482 consecutive HIV-infected patients (mean age 53.9 ± SD 6.9 years; 75% male) attending the multidisciplinary metabolic clinic at the University of Modena, Italy. Frailty was measured with the frailty phenotype and a 37-item FI. RESULTS: The mean FI score was 0.28±0.1 and frailty phenotype categories were: 3.1% frail, 51.9% pre-frail, and 45% robust. The duration of antiretroviral therapy was significantly different across levels of frailty as measured by both frailty tools (P < 0.01), but the nadir CD4 count was only significant for the FI (P = 0.01); current CD4 count was not significantly different across frailty levels using either tool. Both frailty measures were associated with multimorbidity; the FI was associated with Instrumental Activities of Daily Living impairment and falls history, whereas the frailty phenotype was not. CONCLUSIONS: The frailty phenotype and the FI demonstrated similar characteristics in patients at a tertiary-level HIV clinic. The FI had a stronger association with age, nadir CD4 count, comorbidities, falls, and disability. Integrating frailty assessments in clinical practice will be crucial for the development of interventions in age-related conditions, including disability and falls, in older persons living with HIV.
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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 | 0.006 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".