Chapter 13: Primary Screening of Cervical Cancer With Human Papillomavirus Tests
Bibliographic record
Abstract
Despite its history of success in cancer screening, Pap cytology has important limitations, particularly its high false-negative rate, which carries important public health implications. Since the mid-1990s, there has been substantial interest in the use of human papillomavirus (HPV) DNA testing in cervical cancer screening under the premise that the testing of cervical cells for the causative agent of cervical cancer could have acceptable screening performance, while being more reproducible in clinical practice than Pap cytology. There have been several studies assessing the utility of HPV testing compared with the Pap test as a screening tool. These studies varied widely in lesion-outcome definition and in methodology. No studies were based on cervical cancer incidence or mortality. No randomized controlled trials have yet been published; all of the studies were based on concomitant testing for HPV and cytology or additional tests. HPV testing has greater sensitivity (average, 27%) but somewhat lower specificity (average, 8%) than Pap cytology for detecting high-grade lesions. Screening of women aged 30 years or older tends to improve test specificity, but it also does so for cytology. The combination of cytology and HPV attained high-negative predictive values, which suggests that their joint use could allow screening intervals to be safely increased, thus lowering costs. Although evidence is yet to come from long-term studies and from randomized controlled trials with high-grade lesions and invasive cancer as outcomes, HPV testing is clearly one of the most promising new technologies and has the potential to improve cervical cancer-screening effectiveness in many settings.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.079 | 0.038 |
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".