Factors partitioning physical frailty in people aging with HIV: A classification and regression tree approach
Bibliographic record
Abstract
OBJECTIVE: To estimate the extent to which comorbidity and lifestyle factors were associated with physical frailty in middle-aged and older Canadians living with HIV. DESIGN: Cross-sectional analysis of 856 participants from the Canadian Positive Brain Health Now cohort. METHODS: The frailty indicator phenotype was adapted from Fried's criteria using self-report items. Univariate logistic regression and classification and regression tree (CaRT) models were used to identify the most relevant independent contributors to frailty. RESULTS: In all, 100 men (14.0%) and 26 women (19.7%) were identified as frail (≥ 3/5 criteria) for an overall prevalence of 15.2%. Nine comorbidities showed an influential association with frailty. The most influential comorbidities were hypothyroidism [odds ratio (OR) = 2.55, 95% confidence interval (CI): 1.29-5.03] and arthritis (OR = 2.54, 95% CI: 1.58-4.09). Additionally, tobacco (OR = 1.79, 95% CI: 1.05-3.04) showed an association. Any level of alcohol consumption showed a protective effect for frailty. The CaRT model showed nine pathways that led to frailty. Arthritis was the most discriminatory variable followed by alcohol, hypothyroidism, tobacco, cancer, cannabis, liver disease, kidney disease, osteoporosis, lung disease and peripheral vascular disease. The prevalence of physical frailty for people with arthritis was 27.4%; with additional cancer or tobacco and alcohol the prevalence rates were 47.1% and 46.1%, respectively. The protective effect of alcohol consumption evident in the univariate model appeared again in the CaRT model, but this effect varied. Cognitive frailty (19.5% overall) and emotional frailty (37.9% overall) were higher than the prevalence of physical frailty. CONCLUSIONS: Specific comorbidities and tobacco use were implicated in frailty, suggesting that it is comorbidities causing frailty. However, some frailty still appears to be HIV-related. The higher prevalence of cognitive and emotional frailty highlights the fact that physical frailty should not be the only focus in 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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".