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Record W2466398959 · doi:10.1038/nm.4108

International AIDS Society global scientific strategy: towards an HIV cure 2016

2016· article· en· W2466398959 on OpenAlexaff
Steven G. Deeks, Sharon R. Lewin, Anna Laura Ross, Jintanat Ananworanich, Monsef Benkirane, Paula M. Cannon, Nicolas Chomont, Daniel C. Douek, Jeffrey D. Lifson, Ying-Ru Lo, Daniel R. Kuritzkes, David M. Margolis, John W. Mellors, Deborah Persaud, Joseph D. Tucker, Françoise Barré‐Sinoussi, Galit Alter, Judith D. Auerbach, Brigitte Autran, Dan H. Barouch, Georg Behrens, Marina Cavazzana, Zhiwei Chen, Éric A Cohen, Giulio Maria Corbelli, Serge Eholié, Nir Eyal, Sarah Fidler, Laurindo Garcia, Cynthia Grossman, Gail E. Henderson, Timothy J. Henrich, Richard Jefferys, Hans-Peter Kiem, Joseph McCune, Keymanthri Moodley, Peter A. Newman, Monique Nijhuis, Moses Supercharger Nsubuga, Melanie Ott, Sarah Palmer, Douglas D. Richman, Asier Sáez‐Cirión, Matthew Sharp, Janet D. Siliciano, Guido Silvestri, Jerome Amir Singh, Bruno Spire, Jeffrey Taylor, Martin Tolstrup, Susana Valente, Jan van Lunzen, Rochelle P. Walensky, Ira Wilson, Jerome A. Zack

Bibliographic record

VenueNature Medicine · 2016
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsUniversity of TorontoMontreal Clinical Research InstituteUniversité de Montréal
FundersNational Institute of Allergy and Infectious DiseasesNational Institute of Mental HealthMedical Research CouncilNational Cancer InstituteU.S. Department of Health and Human ServicesNational Institutes of HealthWorld Health OrganizationNational Heart, Lung, and Blood InstituteU.S. Department of Defense
KeywordsHuman immunodeficiency virus (HIV)MedicineVirologyPolitical scienceFamily medicineIntensive care medicine

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation 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: Review · Consensus signal: none
Teacher disagreement score0.030
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.012
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0020.002
Scholarly communication0.0070.004
Open science0.0020.007
Research integrity0.0110.009
Insufficient payload (model declined to judge)0.0300.018

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 head0.014
GPT teacher head0.324
Teacher spread0.311 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations422
Published2016
Admission routes1
Has abstractno

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