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Record W2011949244 · doi:10.1258/095646203321264872

Epidemiologic correlates of antibody response to human papillomavirus among women at low risk of cervical cancer

2003· article· en· W2011949244 on OpenAlexaff
B Nonnenmacher, Javier Pintos, Mary Clarisse Bozzetti, I Mielzinska-Lohnas, Attila T. Lörincz, Nilo Ikuta, Gilberto Schwartsmann, Luisa L. Villa, John T. Schiller, Eduardo L. Franco

Bibliographic record

VenueInternational Journal of STD & AIDS · 2003
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsMcGill University
FundersFundação de Amparo à Pesquisa do Estado do Rio Grande do SulCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsSerologyMedicineCervical cancerOdds ratioHPV infectionPopulationAntigenImmunologyAntibodyCancerGynecologyInternal medicineVirology

Abstract

fetched live from OpenAlex

A population at low risk for developing cervical cancer in Southern Brazil was studied to identify the main determinants of serological response to human papillomavirus (HPV). Enzyme-linked immunosorbent assay tests were performed in 976 women to detect serum IgG antibodies against HPV 16 L1 virus-like particles (VLPs) and HPVs 16, 18, 6 and 11 L1 VLPs as a mixture of antigens. Women with four or more sexual partners were more likely to be seropositive than women with one partner (HPV 16 serology odds ratio [OR]=3.06, 95% confidence interval [CI]: 2.0-4.8; HPV 6/11/16/18 serology OR=4.64, 95% CI: 3.0-7.2). HPV DNA and both serological responses were associated. Those positives to HPV 16 serology were twice as likely to have a cytological diagnosis of squamous intraepithelial lesions (SILs) than seronegatives (OR=2.07; 95% CI: 1.0-4.5, and OR=1.73; 95% CI: 0.8-3.8). Seropositivity to HPV 16 and HPV 6/11/16/18 antigens seem to be better markers of past sexual activity than current HPV infection, and humoral response to HPV 16 or HPV 6/11/16/18 may not be a strong indicator of cervical lesions in populations at low risk for cervical lesions.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0070.000

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 head0.029
GPT teacher head0.396
Teacher spread0.367 · 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.

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

Citations30
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of STD & AIDSSame topicCervical Cancer and HPV ResearchFrench-language works237,207