Sedative Co-Medication Patterns Across Frailty States in People with HIV: A Network-Based Study
Bibliographic record
Abstract
Objective To describe sedative co-medication patterns across frailty states (robust, prefrail, frail) in people living with HIV using network-based analysis, aiming to identify key medication interactions and network drivers that can guide safer therapeutic approaches. Methods This cross-sectional study analyzed 321 participants using sedatives from the Positive Brain Health Now Cohort (mean age: 53 years), categorized as robust (30.2%), prefrail (47.0%), or frail (22.7%). Sedative use was classified using the Sedative Load Model, and frailty was assessed with a modified Fried Frailty Phenotype. Co-medication networks were constructed for robust, prefrail, and frail groups, with metrics such as Neighborhood Shift Scores (NESH) and Delta Betweenness used to evaluate network dynamics. Edge-level Observed-to-Expected (O/E) ratios highlighted significant co-prescription patterns and associated drug-drug interactions. Results Frail individuals exhibited the most interconnected network, characterized by higher graph density and average degree compared to robust and prefrail groups. Key ”driver” medications identified were mirtazapine (robust-to-prefrail), gabapentin (robust-to-frail), and pregabalin (prefrail-to-frail). In frail individuals, medication pairs with high O/E ratios (e.g., hydromorphone-clonazepam, O/E: 3.07), posed a potential risk for severe drug interactions requiring therapy modification. Robust individuals displayed fewer and less severe drug interactions, whereas prefrail individuals exhibited an intermediate level of complexity. Conclusion Sedative co-medication patterns vary significantly across frailty states in people with HIV, with frailty amplifying risks of severe drug interactions. Identifying key medications as network drivers provides actionable insights to optimize therapeutic approaches, particularly for depression and neuropathic pain management in prefrail and frail stages.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".