Longitudinal relationship between human papillomavirus infection and the incidence and progression of precursor lesions of cervical neoplasia
Bibliographic record
Abstract
Introduction. Human papillomavirus (HPV) infection is now believed to be the central cause of cervical cancer. However, most of the epidemiological evidence has come from retrospective, case-control studies, which do not provide information on the dynamics of a cumulative or persistent HPV infection. Objectives. (1) To measure the risk of incident neoplastic cervical lesions over time related to prior cumulative and persistent HPV infections. (2) To evaluate the influence of HPV viral burden on lesion risk longitudinally. (3) To estimate the progression rates and sojourn time for precursor squamous intraepithelial lesions (SILs) and how they relate to HPV infection status. Design and methods. In 1993, the Ludwig-McGill study team began a large longitudinal study of the natural history of HPV infection and cervical neoplasia in the city of Sao Paulo, Brazil. Follow-up involved repeated measurements on individual subjects over time. 2462 women were enrolled into the study and were seen every 4 months in the first year (0, 4, 8 and 12 months), and twice yearly thereafter for a period of up to eight years. In addition to obtaining risk factor information via questionnaire, cervical specimens were taken for Pap cytology and HPV testing at every visit. Statistical analyses entailed: (1) using different modalities for defining HPV persistence by type and intensity; (2) using modeling approaches that take into account the repeated measurements of HPV and SIL over time within individuals; (3) analyzing changes in transition states between different cervical lesion grades and the rate of progression from one state to the next. Rationale. A longitudinal, repeated measurement cohort investigation, such as this one, permits an accurate and unbiased assessment of the relationship between cumulative HPV exposure and lesion incidence. An elevated relationship between persistent HPV infections and SIL incidence supports the proposal for the application of type-specific molecular HPV DNA testing as a screening tool for the detection of cervical neoplasia. Better understanding of the natural history of disease can help in developing effective and efficient public health programs in prevention for cervical cancer.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".