In Support of Multidimensional Frailty: A Structural Equation Model from the Canadian Positive Brain Health Now Cohort
Bibliographic record
Abstract
The objective of this study was to estimate the structure and relationships between four h ypothesized frailty dimensions (physical, emotional, cognitive, and social) and the extent to which personal and HIV-related factors and comorbidity associate with these frailty dimensions. This is a secondary analysis of an existing dataset arising from Positive Brain Health Now study (n = 856) in people aging with HIV (mean age: 52.3 ± 8.1 years). Structural equation modeling (SEM) models were applied to two cross-sections of the data: one at study entry and one at second visit, 9-month apart. Multidimensional frailty was modeled based on the combined Wilson–Cleary and International Classification of Functioning, Disability and Health framework. Four dimensions were operationalized with patient-reported and self-report measures from standardized questionnaires. The SEM model from the first visit was replicated using data from the second visit, testing measurement invariance. The proposed model showed acceptable fit at both visits (including no violation of measurement invariance). The final model for the first visit showed that sex, body mass index, HIV diagnosis pre-1997, current or nadir CD4 counts, and comorbidity did not associate with any frailty dimension; however, age (β range: 0.12–0.25), symptoms (β range: −0.35 to −0.58), and measured cognition (β range: 0.10–0.24) directly associated with all frailty dimensions. The model remained stable across the two visits. This study contributes evidence for operationalizing multidimensional frailty. Evidence-based interventions are available for many of the measures considered here, offering opportunities to improve the lives of people with frailty in the context of 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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".