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Record W2118597759 · doi:10.1017/s0950268804002845

Low socioeconomic status and risk for infection with Human Herpesvirus 8 among HIV-1 negative, South African black cancer patients

2004· article· en· W2118597759 on OpenAlexaff
Janet M. Wojcicki, Robert Newton, MI Urban, Lara Stein, Martin Hale, Moosa Patel, Paul Ruff, Ranjan Sur, Dimitra Bourboulia, Freddy Sitas

Bibliographic record

VenueEpidemiology and Infection · 2004
Typearticle
Languageen
FieldMedicine
TopicViral-associated cancers and disorders
Canadian institutionsMcMaster UniversityHamilton Regional Laboratory Medicine Program
Fundersnot available
KeywordsMedicineSocioeconomic statusDemographyProspective cohort studyLogistic regressionCancerInternal medicineAntibodyImmunologyPopulationEnvironmental health

Abstract

fetched live from OpenAlex

Between January 1994 and October 1997, we interviewed 2576 black in-patients with newly diagnosed cancer in Johannesburg and Soweto, South Africa. Blood was tested for HIV-1 and HHV-8 antibodies and the study was restricted to 2191 HIV-1 antibody-negative patients. We examined the relationship between infection with HHV-8 and sociodemographic and behavioural factors using unconditional logistic regression models. Of the 2191 HIV-1 negative patients who did not have Kaposi's sarcoma, 854 (39.1%) were positive for antibodies against the latent nuclear antigen of HHV-8 encoded by orf73 in a immunofluorescence assay. Infection with HHV-8 was independently associated with increasing age (P trend = 0.02). For females, independent risk factors also included working in a paid domestic capacity (OR 1.63, 95% CI 1.09-2.44, P = 0.02), defining occupational status as economically non-active unemployed (OR 1.70, 95% CI 1.06-2.72, P = 0.03), having a state pension or being on a disability grant (OR 1.49, 95% CI 1.05-2.11, P = 0.02), using oral contraceptives (OR 1.43, 95% CI 1.03-1.99, P = 0.03) and having a delayed age at menarche (P trend = 0.04). The relationship between these variables and HHV-8 antibody status requires further, prospective study.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.007
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.015
GPT teacher head0.284
Teacher spread0.269 · 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 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

Citations15
Published2004
Admission routes1
Has abstractyes

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