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Record W2945743173 · doi:10.1177/2325958219850558

Functional Limitations and Disability in Persons Living with HIV in South Africa and United States: Similarities and Differences

2019· article· en· W2945743173 on OpenAlexaff
David M. Kietrys, Hellen Myezwa, Mary Lou Galantino, J. Scott Parrott, Tracy Davis, Todd P Levin, Kelly K. O’Brien, Jill Hanass‐Hancock

Bibliographic record

VenueJournal of the International Association of Providers of AIDS Care (JIAPAC) · 2019
Typearticle
Languageen
FieldMedicine
TopicHIV-related health complications and treatments
Canadian institutionsToronto Rehabilitation InstituteUniversity of Toronto
Fundersnot available
KeywordsDepression (economics)MedicineLogistic regressionActivities of daily livingMedical recordGerontologyDemographyPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

Persons living with HIV (PLHIV) may experience disability. We compared disability among PLHIV in the United States and South Africa and investigated associations with health and demographic characteristics. Secondary analysis of cross-sectional data using medical records and questionnaires including the World Health Organization Disability Assessment Schedule (WHO-DAS) 2.0 12-item version (range: 0-36, with higher scores indicative of more severe disability). Between-country differences for the presence of disability were assessed with logistic regression and differences in severity using multiple regression. Eighty-six percent of US participants reported disability, compared to 51.3% in South Africa. The mean WHO-DAS score was higher in the United States (12.09 ± 6.96) compared to South Africa (8.3 ± 6.27). Participants with muscle pain, depression, or more years since HIV diagnosis were more likely to report disability. Being female or depressed was associated with more severity. Being adherent to anti-retroviral therapy (ART) and employed were associated with less severity. Because muscle pain and depression were predictive factors for disability, treatment of those problems may help mitigate disability in PLHIV.

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)0.001
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 score0.015
Threshold uncertainty score0.209

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.022
GPT teacher head0.253
Teacher spread0.231 · 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

Citations31
Published2019
Admission routes1
Has abstractyes

Explore more

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