Integrating Comprehensive Geriatric Assessment into HIV Care Systems in Indonesia: A Synthesis of Recent Evidence
Bibliographic record
Abstract
Combination antiretroviral therapy (cART) has improved the health and life expectancy of people living with human immunodeficiency virus (HIV). Comorbidities and geriatric syndrome are more prevalent in patients with HIV than in the general population. As a result, people living with HIV may face unique characteristics and needs related to aging. Health-care systems need to prepare to encounter those issues that not only focus on virology suppression and cART management but also chronic non-AIDS comorbidities and geriatric syndrome. However, there are limited data on geriatric assessment among people living with HIV. The purpose of this article is to present findings of a literature search that integrate age-related issues in HIV care management for health-care professionals caring for people living with HIV in Indonesia to consider. Integrating comprehensive geriatric assessment (CGA) into HIV care is essential. However, some critical issues need to be considered prior to implementing CGA in HIV primary care, including social vulnerability, economic inequality, and aging-related stigma. Developing guidelines for implementing CGA in HIV primary clinics remains a priority. Studies of HIV in the aging population in Indonesia need to be conducted to understand the burden of geriatric syndrome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".