Persistent Human Papillomavirus Infection as a Predictor of Cervical Intraepithelial Neoplasia
Bibliographic record
Abstract
CONTEXT: Human papillomavirus (HPV) infection is believed to be the central cause of cervical cancer, although most of the epidemiological evidence has come from retrospective, case-control studies, which do not provide information on the dynamics of cumulative or persistent exposure to HPV infection. OBJECTIVE: To assess the risks of cervical neoplasia related to prior persistent HPV infections. DESIGN AND SETTING: Longitudinal study of the natural history of HPV infection and cervical neoplasia in women residing in the city of São Paulo, Brazil, which was conducted between November 1993 and March 1997 and involved repeated measurements of HPV and lesions with follow-up until June 2000. PARTICIPANTS: A total of 1611 women with no cytological lesions at enrollment and HPV test results available from the first 2 visits. MAIN OUTCOME MEASURE: Cervical specimens taken for Papanicolaou cytology and HPV testing every 4 months in the first year and twice yearly thereafter. Incident cervical cancer precursor lesions ascertained by expert review of all cytology smears. RESULTS: The incidence rate of squamous intraepithelial lesions (SILs) was 0.73 per 1000 women-months (95% confidence interval [CI], 0.5-0.9) among women free of HPV at the 2 initial visits and 8.68 (95% CI, 2.3-15.1) among women with HPV type 16 or 18 infections persisting over both visits. Relative to those negative for HPV oncogenic types at both initial visits, the relative risk (RR) of incident SIL was 10.19 (95% CI, 5.9-17.6) for persistent infections with any known oncogenic HPV types. The equivalent RR of incident high-grade SIL was 11.67 (95% CI, 4.1-33.3). The RRs of lesions were considerably higher for persistent infections with HPV type 16 or 18. CONCLUSION: A strong relationship exists between persistent HPV infections and SIL incidence, particularly for HPV types 16 and 18.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".