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Record W2044935635 · doi:10.1155/2014/365261

Nitrite Inhalants Use and HIV Infection among Men Who Have Sex with Men in China

2014· article· en· W2044935635 on OpenAlexaff
Dongliang Li, Xueying Yang, Zheng Zhang, Qi Xiao, Yuhua Ruan, Yujiang Jia, Stephen W. Pan, Dong Xiao, Z. Jennifer Huang, Fengji Luo, Yifei Hu

Bibliographic record

VenueBioMed Research International · 2014
Typearticle
Languageen
FieldMedicine
TopicMethemoglobinemia and Tumor Lysis Syndrome
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMen who have sex with menMedicineCasualLogistic regressionCross-sectional studyBeijingNitriteIntoxicative inhalantHuman immunodeficiency virus (HIV)DemographyChinaImmunologySyphilisToxicologyInternal medicineBiologyGeographyPathology

Abstract

fetched live from OpenAlex

OBJECTIVE: This is the first study in China to examine the use of nitrite inhalants and its correlates among men who have sex with men (MSM) in Beijing, China. METHODS: A cross-sectional survey was conducted in 2012. Structured interviews collected data on demographics, sexual and drug use behaviors, and the use of HIV services. Blood specimens were collected and tested for HIV and syphilis. RESULTS: A total of 400 MSM eligible for the study were between 19 and 63 years of age and overall HIV prevalence was 6.0% (9.0% among nitrite inhalant users and 3.3% among nonusers). Nearly half (47.3%) of them reported ever using nitrite inhalants and 42.3% admitted using nitrite inhalants in the past year. Multivariable logistic analysis revealed that ever using nitrite inhalants in the past was independently associated with being aged ≤25 years, having higher education attainment, seeking sex via Internet, having casual partners in the past three months, and being HIV positive. CONCLUSION: The use of nitrite inhalants was alarmingly prevalent among MSM in Beijing. The independent association of the nitrite inhalant use with more casual sex partners and HIV infection underscored the need for intervention and prevention of nitrite inhalant use.

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)0.001
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 score0.026
Threshold uncertainty score0.363

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.000
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 head0.035
GPT teacher head0.339
Teacher spread0.304 · 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

Citations38
Published2014
Admission routes1
Has abstractyes

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