Specialist care of older adults with <scp>HIV</scp> infection in the <scp>UK</scp>: a service evaluation
Bibliographic record
Abstract
OBJECTIVES: There is a clear trajectory towards cohort ageing in the UK. HIV infection is associated with an increased prevalence of traditionally age-related comorbidities and geriatric syndromes. Some HIV services have been proactive in innovating models that cater for adapting needs. We aimed to describe how widespread this practice is and what form such services take. METHODS: We conducted an evaluation of the perceived need for and current provision of specialist ageing services, and the need for formal guidance on monitoring or treatment of older adults with HIV infection. A web-based questionnaire was sent to the audit lead at every British HIV Association (BHIVA)-registered HIV clinic. RESULTS: A total of 102 clinics responded, with a broad geographical spread. Five of the 102 clinics have a clinician with an interest in ageing. Two dedicated HIV ageing services exist, practising different models. A quarter (23 of 98; 23%) of clinics reported a need for an ageing service, with three in development. The majority (65 of 95; 68%) supported dedicated guidance for monitoring in older adults, but fewer (39 of 94; 41%) felt that dedicated guidance on treatment was necessary. CONCLUSIONS: We identified two existing and three proposed HIV ageing services. Another 20 clinics (20%) reported an unmet need for a specialist ageing service, suggesting that complex older adults may pose a management challenge. This is the first survey of its kind to attempt to describe the current landscape and opinion around such services. HIV-infected cohorts will continue to age and current models of care may be insufficient, which should prompt services, their users and commissioners to consider what models may best fit current and future demand.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".