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HIV Drug Resistance and HIV Transmission Risk Behaviors Among Active Injection Drug Users

2005· article· en· W2031681081 on OpenAlexaff
Michael J. Kozal, K. Rivet Amico, Jennifer Chiarella, Deborah H. Cornman, William A. Fisher, Jeffrey D. Fisher, Gerald Friedland

Bibliographic record

VenueJAIDS Journal of Acquired Immune Deficiency Syndromes · 2005
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsWestern University
FundersNational Institute of Mental Health
KeywordsDrugMedicineDrug resistanceHIV drug resistanceDrug injectionHuman immunodeficiency virus (HIV)Transmission (telecommunications)Needle sharingLentivirusViral loadPharmacologyImmunologyViral diseaseAntiretroviral therapyBiology

Abstract

fetched live from OpenAlex

HIV(+) injection drug users in clinical care may harbor and transmit drug-resistant HIV. We performed a retrospective study of HIV drug resistance and risk behavior among HIV(+) injection drug users in care to determine the number of needle-sharing events that involved and the proportion of sharing partners exposed to drug-resistant HIV. Among 180 HIV injection drug users, 55 (31%) reported injecting drugs in the previous month, and 22 of these (40%) shared needles and/or works 148 times with 296 partners, of whom 271 (92%) were thought to be HIV(-) or status unknown. Further, 55 (31%) drug users harbored resistant HIV, including 5 (3% of total) who also shared needles and/or works a total of 27 times with 44 partners (18% of all sharing events and 15% of all exposed partners). A small proportion of injection drug users receiving clinical care engage in injection risk behavior and carry resistant HIV; however, because of multiple partners and needle-sharing events, they expose a substantial number of individuals to drug-resistant HIV. Strategies to reduce injection drug use risk behaviors among patients in clinical care are needed to reduce the transmission of sensitive and resistant HIV.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.508
Threshold uncertainty score0.909

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.007
GPT teacher head0.241
Teacher spread0.234 · 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

Citations35
Published2005
Admission routes1
Has abstractyes

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