WHAT KIND OF HEALTH SCREENING IS USEFUL FOR PERSONS AGING WITH HIV?
Bibliographic record
Abstract
Abstract Background In the United States, 54% of people aging with HIV (PAWH) are age >/= 50 and 89.7% are virally suppressed. HIV is linked to increased risk of developing comorbidities and may accelerate aging. This population may not receive adequate screening for changes related to advancing age. We describe initial findings of a “Maturity Screen” for PAWH. Methods Setting and population. Convenience sample of PAWH aged 50 years or older receiving care at an HIV specialty clinic in a major metropolitan area. Patients completed a series of measures administered by trained care team members. Measures. sociodemographic (e.g., age, sex), comorbid conditions. Maturity Screen: MoCA, PHQ2, ADL, IADL, GAD2, gait speed and Edmonton Frail Scale. Results 82 participants were screened to-date; 41 males, 36 females, 4 unknown. Mean age 63 (SD: 7.6 yrs.); mean # comorbid conditions 1.8 (SD: 1.3, range 0-5); top four comorbidities were hyperlipidemia (56%), hypertension (43%), diabetes (11%), and CKD (11%). Mean MoCA 24.9 (SD: 3.7, range 15-30); mean PHQ2 0.6 (SD: 1.2); ADL 5.9 (SD: 0.5); IADL 7.8 (SD: 0.8, max score 9). Conclusion In this sample of PAWH maturity screening suggests healthier group than anticipated and low levels of depression. Borderline mean cognitive screen suggests need to monitor for emerging issues likely to impact quality of life. Examining social support in the context of PAWH and potential cognitive decline may identify those with increased future healthcare needs and poor outcomes. Gait speed and frailty measures are currently being analyzed and may identify additional functional needs.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".