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Record W2522211018 · doi:10.1016/s2055-6640(20)30923-7

IAS Towards an HIV Cure Symposium: people focused, science driven 18–19 July 2015, Vancouver, Canada

2015· article· en· W2522211018 on OpenAlexafffundabout
Sarah Fidler, John Thornhill, Eva Malatinková, Robert J. Reinhard, Rosanne Lamplough, Jintanat Ananworanich, Ann Chahroudi

Bibliographic record

VenueJournal of Virus Eradication · 2015
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsMontreal Clinical Research InstituteUniversity of Toronto
FundersCHIST-ERAMedical Research CouncilCanadian Institutes of Health ResearchSanofi PasteurAgence Nationale de Recherches sur le Sida et les Hépatites ViralesNational Institutes of HealthSanofiSidactionStyrelsen för Internationellt UtvecklingssamarbeteGilead SciencesViiV HealthcareGlaxoSmithKlineBristol-Myers Squibb
KeywordsHuman immunodeficiency virus (HIV)GerontologyMedicineHistoryFamily medicine

Abstract

fetched live from OpenAlex

The International AIDS Society (IAS) convened the Towards an HIV Cure Symposium on 18-19 July 2015 in Vancouver, Canada, bringing together researchers and community to discuss the most recent advances in our understanding of HIV latency, reservoirs and a summary of the current clinical approaches towards an HIV cure. The symposium objectives were to: (1) gather researchers and stakeholders to present, review, and discuss the latest research towards an HIV cure; (2) promote cross-disciplinary global interactions between basic, clinical and social scientists; and (3) provide a platform for sharing information among scientists, clinicians, funders, media and civil society. The symposium examined basic molecular science and animal model data, and emerging and ongoing clinical trial results to prioritise strategies and determine the viral and immune responses that could lead to HIV remission without antiretroviral therapy. This report summarises some of the major findings discussed during the symposium.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.034
GPT teacher head0.347
Teacher spread0.314 · 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 designNot applicable
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

Citations2
Published2015
Admission routes3
Has abstractyes

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