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Record W2154381494 · doi:10.1177/2325957413488196

French HIV Experts on Early Antiretroviral Treatment for Prevention

2013· article· en· W2154381494 on OpenAlexafffund
Bertrand Lebouché, Kim Engler, Joseph J. Lévy, Norbert Gilmore, Bruno Spire, Willy Rozenbaum, Tinhinane Lacene, Jean‐Pierre Routy

Bibliographic record

VenueJournal of the International Association of Providers of AIDS Care (JIAPAC) · 2013
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversité du Québec à MontréalMcGill University Health Centre
FundersCanadian Institutes of Health ResearchMcGill University
KeywordsCandidacyTreatment as preventionPreparednessAntiretroviral therapyMedicineAntiretroviral treatmentHuman immunodeficiency virus (HIV)LegitimacyPopulationFamily medicineEnvironmental healthPolitical scienceViral load

Abstract

fetched live from OpenAlex

Early use of highly active antiretroviral treatment (ART) in people living with HIV for HIV prevention has gained legitimacy but remains controversial. Nineteen French HIV experts with diverse specializations (over half of whom were clinicians) were qualitatively interviewed on their views about ART irrespective of CD4 count of more than 500 cells/mm3 for purposes of HIV prevention, which is not systematically recommended in France. Content analysis identified 2 broad categories: individual considerations (subcategories: patient health and well-being; patient preparedness and choice) and collective considerations (subcategories:HIV transmission risk; impact on the epidemic; cost). Uncertainty surrounded many experts' considerations, and unity was lacking on key issues (eg, candidacy for early preventive treatment, expected clinical- and population-level effects). An umbrella theme labeled "Weighing the merits of early ART in the face of uncertainties was identified. Our analyses raise doubts about the current acceptability of widespread implementation of early ART for HIV prevention in France.

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)0.002
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 score0.292
Threshold uncertainty score0.324

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
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 head0.020
GPT teacher head0.319
Teacher spread0.299 · 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

Citations7
Published2013
Admission routes2
Has abstractyes

Explore more

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