Screening tools for targeted comprehensive geriatric assessment in HIV-infected patients 50 years and older
Bibliographic record
Abstract
Many people living with HIV (PLWH) are aging with geriatric syndromes, but few undergo comprehensive geriatric assessment (CGA) due to limited resources. Our study evaluates tools to identify aging PLWH who may forego CGA. We conducted a cross-sectional study on 357 PLWH ≥50 years old at the Red Cross, Thailand. Tools evaluated were the Veterans Aging Cohort Study Index (VACSI) and G-8, which is predictive among older cancer patients. CGA consists of eight tests: history of fall within 12 months, timed-up-and-go test (TUG), activities of daily living (ADL), instrumental ADL (IADL), Montreal cognitive assessment (MoCA), Thai depression scale (TDS), mini nutritional assessment (MNA), and HIV symptom index (HSI). We considered ≥2 impaired domains on CGA to be abnormal results. Forty-nine percent (n = 175) had ≥2 impaired domains on CGA. Few participants had experienced a fall (11%) or abnormal TUG/ADL/IADL (<2%), and only MoCA/TDS/MNA/HSI were analyzed. A VACSI < 17 produces 85% sensitivity (Se) and 30% specificity (Sp) (area under the ROC curve [AUC] = 63, 95%CI 58–69) and G-8 > 15.5 produces 90%Se and 33%Sp (AUC = 74, 95%CI 69–79) in identifying patients with <2 impaired domains. A G-8 > 13.5 produces 91%Se and 77%Sp (AUC = 89, 95%CI 86–92) in ruling out abnormal nutrition. Patients with VACSI < 17 and G-8 > 15.5 may forego CGA due to low likelihood of abnormal cognition, mood, nutrition, or symptom burden.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".