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Record W2993492863 · doi:10.5770/cgj.22.387

An Emerging Concern—High Rates of Frailty among Middle-aged and Older Individuals Living with HIV

2019· article· en· W2993492863 on OpenAlexaffvenueabout
Jacqueline M. McMillan, Hartmut B. Krentz, M. John Gill, David B. Hogan

Bibliographic record

VenueCanadian Geriatrics Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsAlberta Hip and Knee ClinicUniversity of Calgary
Fundersnot available
KeywordsMedicineHuman immunodeficiency virus (HIV)GerontologyFrailty IndexDemographyCross-sectional studyFamily medicine

Abstract

fetched live from OpenAlex

Background The aim of the present study was to calculate a frailty index (FI) in older adults (≥50) living with HIV, search for cross-sectional associations with the FI, and investigate the association between the FI score and two-year mortality. Methods Cross-sectional study with a short-term prospective com-ponent for the determination of two-year mortality was performed. The study took place in an HIV outpatient clinic in Calgary, Canada between November 1, 2016 and December 31, 2018. Over 700 patients 50 years of age or older took part. We calculated a FI for each patient, examined associations between FI and select patient characteristics, and evaluated the association between FI value and two-year mortality.Results The mean FI was 0.303 (± 0.128). Mean FI did not differ between males and females, nor was it associated with either nadir or current CD4 cell count. It did increase with age, duration of ART, and duration of diagnosed HIV infection. Mean FI was higher among those who died compared to survivors (0.351 vs. 0.301; p=.033). ConclusionsFrailty is highly prevalent in persons living with HIV and associated with a higher mortality rate. Health-care providers should be aware of the earlier occurrence of frailty in adults living with HIV.

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)0.001
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 score0.012
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.262
Teacher spread0.238 · 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

Citations11
Published2019
Admission routes3
Has abstractyes

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