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Record W2023727325 · doi:10.1016/j.jfda.2013.09.020

HIV: Seek, test, treat, and retain

2013· article· en· W2023727325 on OpenAlexaff
Jacques Normand, Julio Montaner, Chi‐Tai Fang, Zunyou Wu, Yi‐Ming Chen

Bibliographic record

VenueJournal of Food and Drug Analysis · 2013
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsAIDS VancouverUniversity of British Columbia
FundersNational Institute on Drug Abuse
KeywordsHuman immunodeficiency virus (HIV)Test (biology)MedicineHiv testFamily medicinePresentation (obstetrics)PopulationSubstance abuseTraditional medicinePsychologyGerontologyPsychiatryEnvironmental healthSurgeryHealth services

Abstract

fetched live from OpenAlex

The "HIV: Seek, Test, Treat, and Retain" session was chaired by Dr. Jacques Normand, the Director of AIDS Research at the U.S. National Institute on Drug Abuse. Dr. Yi-Ming Chen served as the discussant. The three presenters (and their presentation topics) were: Dr. Julio Montaner (Treatment as Prevention-The Key to an AIDS-free Generation), Dr. Chi-Tai Fang (Population-level Effect of Free Access to HAART on Reducing HIV Transmission in Taiwan), and Dr. Zunyou Wu (Challenges in Promoting HIV Test & Treat Strategy in China).

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)0.000
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 score0.433
Threshold uncertainty score0.583

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.011
GPT teacher head0.285
Teacher spread0.274 · 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

Citations6
Published2013
Admission routes1
Has abstractyes

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