A frailty index predicts survival and incident multimorbidity independent of markers of HIV disease severity
Bibliographic record
Abstract
OBJECTIVES: Aging with HIV is associated with multisystem vulnerability that might be well characterized by frailty. We sought to construct a frailty index based on health deficit accumulation in a large HIV clinical cohort and evaluate its validity including the ability to predict mortality and incident multimorbidity. DESIGN AND METHODS: This is an analysis of data from the prospective Modena HIV Metabolic Clinic cohort, 2004-2014. Routine health variables were screened for potential inclusion in a frailty index. Content, construct, and criterion validity of the frailty index were assessed. Multivariable regression models were built to investigate the ability of the frailty index to predict survival and incident multimorbidity (at least two chronic disease diagnoses) after adjusting for known HIV-related and behavioral factors. RESULTS: Two thousand, seven hundred and twenty participants (mean age 46 ± 8; 32% women) provided 9784 study visits; 37 non-HIV-related variables were included in a frailty index. The frailty index exhibited expected characteristics and met validation criteria. Predictors of survival were frailty index (0.1 increment, adjusted hazard ratio 1.63, 95% confidence interval 1.05-2.52), current CD4 cell count (0.48, 0.32-0.72), and injection drug use (2.51, 1.16-5.44). Predictors of incident multimorbidity were frailty index (adjusted incident rate ratio 1.98, 1.65-2.36), age (1.07, 1.05-1.09), female sex (0.61, 0.40-0.91), and current CD4 cell count (0.71, 0.59-0.85). CONCLUSION: Among people aging with HIV in northern Italy, a frailty index based on deficit accumulation predicted survival and incident multimorbidity independently of HIV-related and behavioral risk factors. The frailty index holds potential value in quantifying vulnerability among people aging 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 | 0.002 | 0.007 |
| 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 | 0.000 |
| 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".