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Record W4300457025 · doi:10.17615/38d4-sd12

Body Mass Index and Early CD4+ T-cell Recovery among Adults Initiating Antiretroviral Therapy in North America, 1998–2010

2020· article· en· W4300457025 on OpenAlexfundno aff
CA Jenkins, Sonia Napravnik, MJ Silverberg, Jill Gill, H. Richard Crane, Todd T. Brown, B Lau, JR Koethe, T. R. Sterling, BE Shepherd, Samuel E. Stinnette, Andrea P - Anema, Aaron J. Blashill, A Willig

Bibliographic record

VenueUNC Libraries · 2020
Typearticle
Languageen
FieldMedicine
TopicHIV-related health complications and treatments
Canadian institutionsnot available
FundersNational Center for Research ResourcesCanadian Institutes of Health ResearchCenter for AIDS Research, University of WashingtonHealth Resources and Services AdministrationCenters for Disease Control and PreventionNational Cancer InstituteAgency for Healthcare Research and QualityNational Institutes of HealthNational Institute of Allergy and Infectious DiseasesGovernment of AlbertaVanderbilt University
KeywordsAntiretroviral therapyBody mass indexIndex (typography)Human immunodeficiency virus (HIV)GerontologyDemographyMedicinePsychologyVirologyInternal medicineViral loadSociologyComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

Adipose tissue affects several aspects of the cellular immune system, but prior epidemiologic studies have differed on whether a higher body mass index (BMI) promotes CD4+ T-cell recovery on antiretroviral therapy (ART). The objective of this analysis was to assess the relationship between BMI at ART initiation and early changes in CD4 T-cell count.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.938
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.232
Teacher spread0.216 · 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 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

Citations4
Published2020
Admission routes1
Has abstractyes

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