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Record W2526314821 · doi:10.1093/infdis/jiw459

Cigarette Smoking and Inflammation, Monocyte Activation, and Coagulation in HIV-Infected Individuals Receiving Antiretroviral Therapy, Compared With Uninfected Individuals

2016· article· en· W2526314821 on OpenAlexaff
Katherine W. Kooij, Ferdinand W.N.M. Wit, Thijs Booiman, Marc van der Valk, Maarten F. Schim van der Loeff, Neeltje A. Kootstra, Peter Reiss

Bibliographic record

VenueThe Journal of Infectious Diseases · 2016
Typearticle
Languageen
FieldMedicine
TopicHIV-related health complications and treatments
Canadian institutionsInstitute of Infection and Immunity
FundersJanssen PharmaceuticalsViiV HealthcareZonMwAids FondsGilead SciencesBristol-Myers Squibb
KeywordsMedicineAntiretroviral therapyInflammationHuman immunodeficiency virus (HIV)ImmunologyCoagulationMonocyteCigarette smokingViral loadInternal medicine

Abstract

fetched live from OpenAlex

Smoking may affect cardiovascular disease risk more strongly in human immunodeficiency virus (HIV)-infected individuals than HIV-uninfected individuals. We hypothesized that an interaction at the level of the immune system may contribute to this increased risk. We assessed soluble markers of inflammation (high-sensitivity C-reactive protein [hsCRP]), immune activation (soluble [s]CD14 and sCD163), and coagulation (D-dimer) in HIV-infected and uninfected never, former, and current smokers. Smoking was independently associated with higher hsCRP levels and lower sCD163 levels and was borderline significantly associated with higher sCD14 and D-dimer levels. We found no evidence of a differential effect of smoking in HIV-infected individuals as compared to uninfected individuals.

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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.273
Teacher spread0.258 · 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

Citations25
Published2016
Admission routes1
Has abstractyes

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