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Record W4307451954 · doi:10.1093/cid/ciac835

Broadly Neutralizing Antibodies for Human Immunodeficiency Virus Treatment: Broad in Theory, Narrow in Reality

2022· article· en· W4307451954 on OpenAlexaff
Laura Waters, Rosa de Miguel Buckley, Sébastien Poulin, José Ramón Arribas

Bibliographic record

VenueClinical Infectious Diseases · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsCentre Intégré de Santé et de Services Sociaux des Laurentides
Fundersnot available
KeywordsMedicineHuman immunodeficiency virus (HIV)AntibodyAntiretroviral therapyImmunologyVirologyClinical trialNeutralizing antibodyVirusIntensive care medicineViral loadInternal medicine

Abstract

fetched live from OpenAlex

In this viewpoint, we briefly review the status of antiretroviral therapy (ART), its unmet needs, and the role that broadly neutralizing antibodies (bNAbs) might have in the near future for the treatment of human immunodeficiency virus (HIV). We summarize advances in the development of bNAbs as antiretroviral therapy, the results of main clinical trials of bNAbs for HIV treatment and prevention, and its role in cure trials. The limitations of broadly neutralizing antibodies are the current need for primary resistance testing, the still unclear number of antibodies that must be combined, the lack of penetration in anatomical reservoirs, and the role they might play in cure studies. We compare the advantages and disadvantages of "classical ART" and therapy based on broadly neutralizing antibodies. We conclude that broadly neutralizing antibodies still need considerable improvements before they can be considered an alternative to classical ART.

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 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)0.006
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0030.004
Open science0.0010.002
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0020.001

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 head0.062
GPT teacher head0.411
Teacher spread0.348 · 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 designTheoretical or conceptual
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

Citations10
Published2022
Admission routes1
Has abstractyes

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