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Record W2113641481

A cumulative case-control study of risk factor profiles for oncogenic and nononcogenic cervical human papillomavirus infections.

2000· article· en· W2113641481 on OpenAlexaff
Marie Rousseau, Eduardo L. Franco, Luisa L. Villa, João P. Sobrinho, Lara Termini, Jose M. Arizon-Del Prado, Thomas E. Rohan

Bibliographic record

VenuePubMed · 2000
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsCervical cancerHPV infectionHuman papillomavirusRisk factorEpidemiologyEtiologyCumulative riskTransmission (telecommunications)PapillomaviridaeMedicineCancerVirologyBiologyGynecologyImmunologyInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

Human papillomaviruses (HPVs) play an essential role in the etiology of cervical cancer, but besides an established role for sexual transmission, little is known about other risk factors for HPV infection. Risk factors for nononcogenic, oncogenic, and HPV 16 cervical infections were investigated using a cumulative case-control approach nested in an ongoing cohort study of low income women from São Paulo, Brazil. HPV DNA was detected and typed by the MY09/11 PCR protocol. Risk factor information was obtained via interviews. In a case-control analysis, we compared women who harbored infections with exclusively nononcogenic types (n = 123), exclusively oncogenic types (n = 94), and any HPV 16 (n = 60) to women remaining HPV-negative (n = 512) throughout 1 year of follow-up. A strong negative association was found between age and oncogenic infections, but not with nononcogenic infections. Oral contraceptive use was strongly and exclusively associated with oncogenic and HPV 16 infections. Markers of sexual activity were associated with all types of infections, although with varying strengths. Our results suggest some important differences in the epidemiological correlates of HPV infection according to oncogenicity that may have implications for the-planning of specific preventive strategies aiming at reduction of cervical cancer risk.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.787
Threshold uncertainty score0.999

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)0.0020.000

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 head0.050
GPT teacher head0.339
Teacher spread0.289 · 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

Citations77
Published2000
Admission routes1
Has abstractyes

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