Assessing the time dependence of prognostic values of cytology and human papillomavirus testing in cervical cancer screening
Bibliographic record
Abstract
Accurate assessment of risks for developing cervical intraepithelial neoplasia of grade 2 or worse (CIN2+) after a given set of screening test results is instrumental to reaching valid conclusions and informing cervical cancer screening recommendations. Using data from the Canadian Cervical Cancer Screening Trial (CCCaST), we assessed prognostic values of enrollment screening test results to predict CIN2+ among women attending routine cervical screening using multivariable Cox proportional hazards (PH) regression and its flexible extension during each of two follow-up periods (protocol-defined and extended). Nonproportional (time-dependent (TD)) and/or nonlinear effects were modeled, as appropriate. Women with abnormal cytology had hazard ratios (HRs) for CIN2+ detection of 17.61 (95% CI: 11.25-27.57) and 10.46 (95% CI: 5.41-20.24) relative to women with normal cytology during the protocol-defined and extended follow-up periods, respectively. High-risk human papillomavirus (HR-HPV) positivity was an even stronger predictor of CIN2+ risk, with significant TD effects during both follow-up periods (p <0.001 for both TD effects). Risks among women co-testing HR-HPV+ with and without abnormal cytology (relative to women co-testing negative) were highest immediately after baseline, and decreased significantly thereafter (p <0.001 for both TD effects). HRs for HPV16+ and HPV18+ women (relative to those testing HR-HPV-) did not vary significantly over time (HR = 182.96; 95% CI: 95.16-351.77 and HR = 111.81; 95% CI: 44.60-280.31, respectively). Due to TD effects, conventional Cox model estimates considerably underestimated adjusted HRs associated with positive HR-HPV testing results early on in the follow-up periods.
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 | 0.016 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".