A cumulative case-control study of risk factor profiles for oncogenic and nononcogenic cervical human papillomavirus infections.
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".