Frailty in Men Living with HIV: A Cross-Sectional Comparison of Three Frailty Instruments
Bibliographic record
Abstract
Background Potent antiretroviral treatment has resulted in near normal life expectancy for people living with HIV. Consequently, there is an increased focus on comorbidities, frailty and quality of life. Methods We assessed and compared the prevalence of frailty, associated factors and relationship with quality of life in older Australian men living with HIV in a cross-sectional study using three frailty measurements. The Frailty Phenotype, Frailty Index and Edmonton Frail Scale were applied to 93 HIV-infected men aged over 50 years, on antiretroviral therapy. Multivariable ordinal logistic regression was used to analyse the associations of frailty with covariates and quality of life. Results The prevalence of frailty was 10.8% ( n=10) using the Frailty Phenotype; 22.6% ( n=21) using the Frailty Index and 15.1% ( n=14) using the Edmonton Frail Scale. Frailty Phenotype-defined pre-frailty/frailty was associated with pre-1996 ART initiation (OR, 3.56; CI, 1.23, 10.36; P=0.020) and depression (OR, 3.74; CI, 1.24, 11.27; P=0.019). Osteoporosis, serious non-AIDS events and AIDS were associated with Frailty Index-defined frailty (OR, 4.84, CI, 1.27, 18.43, P=0.021; OR, 4.27, CI, 1.25, 14.58, P=0.020; OR, 4.62, CI, 1.30, 16.45, P=0.018, respectively) and Edmonton Frail Scale-defined frailty (OR, 7.51; CI, 1.55, 36.42; P=0.012; OR, 7.71; CI, 1.62, 36.75; P=0.010; OR, 8.53; CI, 1.70, 42.73; P=0.009, respectively), independent of age and current CD4 + T-cell count. Frailty, defined by any of the instruments, was significantly associated with poorer quality of life ( P<0.001). Conclusions Identifying frailty is an increasingly important contemporary consideration of HIV care related to ageing and quality of life.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".