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Record W2804639574 · doi:10.1007/s10461-018-2141-2

An Empiric Risk Score to Guide PrEP Targeting Among MSM in Coastal Kenya

2018· article· en· W2804639574 on OpenAlexaff
Elizabeth Wahome, Alexander N. Thiong’o, Grace Mwashigadi, Oscar Chirro, Khamisi Mohamed, Evans Gichuru, John Mwambi, Matt A. Price, Susan M. Graham, Eduard J. Sanders

Bibliographic record

VenueAIDS and Behavior · 2018
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsSimon Fraser University
FundersNational Institute of Allergy and Infectious DiseasesNational Institute of Mental HealthNational Institutes of HealthWellcome Trust
KeywordsHealth psychologyPublic healthMedicineEnvironmental healthFamily medicineDemographyNursingSociology

Abstract

fetched live from OpenAlex

Men who have sex with men (MSM), who have heterogeneous HIV-acquisition risks are not specifically targeted in Kenyan pre-exposure prophylaxis (PrEP) guidelines. We used data from an open cohort, which followed 753 initially HIV-negative MSM participants for more than 1378.5 person-years, to develop an empiric risk score for targeting PrEP delivery. Independent predictors of incident HIV-1 infection in this cohort were an age of 18-24 years, having only male sex partners, having receptive anal intercourse, having any unprotected sex, and having group sex. Poisson model coefficients were used to assign a numeric score to each statistically significant predictor. A risk score of ≥ 1 corresponded to an HIV-1 incidence of ≥ 2.2 [95% confidence interval (CI) 1.2-4.1] and identified 81.3% of the cohort participants as being at high risk for HIV-1 acquisition. The area under the receiver operating characteristic curve was 0.76 (95% CI 0.71-0.80). This empiric risk score may help Kenyan health care providers to assess HIV-1 acquisition risk and encourage PrEP uptake by high-risk MSM.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.027
GPT teacher head0.371
Teacher spread0.344 · 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 source (direct Gemma or distilled Codex), 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

Citations39
Published2018
Admission routes1
Has abstractyes

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