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Record W2128600987 · doi:10.1177/2325957414567682

Factors Associated with Late Engagement to HIV Care in Western Kenya

2015· article· en· W2128600987 on OpenAlexaffabout
Charles Kwobah, Paula Braitstein, Julius Koech, Gilbert Simiyu, Ann Mwangi, Kara Wools‐Kaloustian, Abraham Siika

Bibliographic record

VenueJournal of the International Association of Providers of AIDS Care (JIAPAC) · 2015
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsPublic Health OntarioUniversity of Toronto
FundersU.S. President’s Emergency Plan for AIDS ReliefUnited States Agency for International Development
KeywordsMedicineConfidence intervalQuarter (Canadian coin)Human immunodeficiency virus (HIV)Odds ratioDemographyHealth careGerontologyFamily medicineInternal medicineGeography

Abstract

fetched live from OpenAlex

Background: Late presentation of patients contributes significantly to the high mortality reported in HIV -care and treatment programs in sub-Saharan Africa. Methods: A cross-sectional study was conducted to assess factors associated with late engagement to HIV care at the Academic Model Providing Access to Healthcare in western Kenya. Late engagement was defined as baseline CD4 ≤100 cells/mm 3 . Results: Of the 10 533 participants included in the analysis, 67% were female and mean age was 36.7 years. Overall, 23% of the participants presented late. Factors associated with late engagement included male gender (adjusted odds ratio [AOR]: 1.54, 95% confidence interval [CI]: 1.35-1.75), older age (AOR: 1.62, 95% CI: 1.02-2.56), and longer travel time to clinic (AOR: 1.18, 95% CI: 1.04-1.34). Conclusion: Nearly one-quarter of HIV-infected patients in our setting present with advanced immune suppression at initial encounter. Being male, older age, and living further away from clinic are associated with late engagement to care.

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)0.005
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 score0.128
Threshold uncertainty score0.621

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
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 head0.041
GPT teacher head0.324
Teacher spread0.283 · 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

Citations20
Published2015
Admission routes2
Has abstractyes

Explore more

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