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Record W2019554379 · doi:10.4172/2155-6113.s4-e001

Are Low- and Middle-Income Countries Repeating Mistakes Made by High-Income Countries in the Control of HIV for Men who have Sex with Men?

2012· article· en· W2019554379 on OpenAlexaboutno aff
Han‐Zhu Qian

Bibliographic record

VenueJournal of AIDS & Clinical Research · 2012
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious Diseases
KeywordsMedicineLow and middle income countriesHuman immunodeficiency virus (HIV)High income countriesDeveloping countryMen who have sex with menLow incomeDemographyEnvironmental healthDemographic economicsEconomic growthVirologyEconomicsSyphilis

Abstract

fetched live from OpenAlex

Men who have sex with men (MSM) have been represented disproportionately in the HIV epidemic in high income countries since the first HIV/AIDS cases were reported in MSM in 1981. Among all vulnerable HIV populations, MSM account for the preponderance of prevalent AIDS cases in Western Europe [1,2]. Similarly, the largest numbers of persons with newly diagnosed HIV infections (range 45%–65%) are MSM in the United States, Canada, Australia, and New Zealand [3–7]. In contrast, in many low- and middle-income country (LMICs) HIV epidemics were driven by injection drug use (IDU), heterosexual sex, and/or contaminated blood collection and transfusion [8,9]. In recent years, rapid increases in the HIV epidemic among MSM have been observed LMICs in Asia [10], Africa [11], South America [12] and Eastern Europe and Central Asia [13]. For example, MSM account for nearly one third of prevalent AIDS cases in Thailand [14] and Brazil [15], and 30 % – 75 % of estimated new HIV infections in various parts of Laos and China [16,17]. A small number of epidemiologic studies have also shown high incidence (6.8/100 person-years)

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)0.021
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.154
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0210.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.076
GPT teacher head0.440
Teacher spread0.364 · 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.

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

Citations3
Published2012
Admission routes1
Has abstractyes

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