Modeling the Time Dependence of the Association between Human Papillomavirus Infection and Cervical Cancer Precursor Lesions
Bibliographic record
Abstract
The authors studied the time-dependent association between human papillomavirus (HPV) infection and squamous intraepithelial lesions (SIL) among women enrolled in a cohort study in Brazil (1993-2002), using repeated Papanicolaou cytologic examination and HPV testing by polymerase chain reaction. Through simulation with conceivable alternative cohort designs, they investigated different regression modeling approaches using time-varying covariates, time-varying hazard ratio functions, and repeated events to assess the effect of delay in lesion detection. Associations between HPV and early SIL were of high magnitude. The age-adjusted hazard ratios for the association between HPV at enrollment and low-grade SIL decreased gradually with time until 72 months for both oncogenic types of HPV (hazard ratio = 3.96, 95% confidence interval (CI): 2.5, 6.4) and nononcogenic types (hazard ratio = 2.37, 95% CI: 1.3, 4.3). The hazard ratio for incident high-grade SIL remained constant, ranging from 7.15 (95% CI: 2.0, 25.1) at 12 months to 6.26 (95% CI: 2.7, 14.5) at 72 months for oncogenic types of HPV. With oncogenic HPV as the time-dependent predictor variable, the hazard ratios for incident SIL and high-grade SIL events were 14.2 (95% CI: 8.7, 23.1) and 32.7 (95% CI: 8.4, 127.3), respectively. Investigators may underestimate the prognostic value of HPV detection using designs that rely on HPV ascertainment at a single time point. The waning in hazard ratios should be considered in the implementation of HPV testing-based screening programs.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".