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Record W3208235317 · doi:10.1111/hiv.13193

What problems associated with ageing are seen in a specialist service for older people living with HIV?

2021· article· en· W3208235317 on OpenAlexaff
Howell T. Jones, Alim Samji, Nigel Cope, Joanne Williams, Leonie Swaden, Abhishek Katiyar, Fiona Burns, Aisha McClintock‐Tiongco, Margaret Johnson, Tristan Barber

Bibliographic record

VenueHIV Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicHIV-related health complications and treatments
Canadian institutionsInstitute of Infection and Immunity
FundersNational Institute for Health and Care Research
KeywordsMedicineGerontologyHuman immunodeficiency virus (HIV)Older peopleIndependent livingPopulation ageingPopulationActivities of daily livingService (business)Multidisciplinary approachFamily medicinePsychiatryEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVES: By 2030 the majority of the people living with HIV in the United Kingdom will be over the age of 50. HIV services globally must adapt to manage people living with HIV as they age. Currently these services are often designed based on data from the wider population or from the experiences of HIV clinicians. This article aims to help clinicians designing inclusive HIV services by presenting the most common needs identified during the first year of a specialist clinic for older people living with HIV at the Ian Charleson Day Centre, Royal Free Hospital in London, United Kingdom. METHODS: The records of all thirty-five patients attending the inaugural nine sessions were reviewed. RESULTS: The median age of attendees was 69 (53-93) with 77% being male, 63% being White, 49% being heterosexual and 97% being virally suppressed respectively. The majority (83%) met the criteria for frailty using the Fried frailty phenotype. Eighteen issues linked to ageing were identified with the most common being affective symptoms (51%), memory loss (37%) and falls (29%). CONCLUSIONS: Whilst older people living with HIV are a heterogeneous group frailty is common and appears to present earlier. HIV services either need to adapt to meet these additional needs or must support users in transitioning to existing services. We feel that our multidisciplinary model is successful in identifying problems associated with ageing in people living with HIV and could be successfully replicated elsewhere.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.603

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.288
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2021
Admission routes1
Has abstractyes

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