Guidelines for human papillomavirus DNA test requirements for primary cervical cancer screening in women 30 years and older
Bibliographic record
Abstract
Given the strong etiologic link between high-risk HPV infection and cervical cancer high-risk HPV testing is now being considered as an alternative for cytology-based cervical cancer screening. Many test systems have been developed that can detect the broad spectrum of hrHPV types in one assay. However, for screening purposes the detection of high-risk HPV is not inherently useful unless it is informative for the presence of high-grade cervical intraepithelial neoplasia (CIN 2/3) or cancer. Candidate high-risk HPV tests to be used for screening should reach an optimal balance between clinical sensitivity and specificity for detection of high-grade CIN and cervical cancer to minimize redundant or excessive follow-up procedures for high-risk HPV positive women without cervical lesions. Data from various large screening studies have shown that high-risk HPV testing by hybrid capture 2 and GP5+/6+-PCR yields considerably better results in the detection of CIN 2/3 than cytology. The data from these studies can be used to guide the translation of high-risk HPV testing into clinical practice by setting standards of test performance and characteristics. On the basis of these data we have developed guidelines for high-risk HPV test requirements for primary cervical screening and validation guidelines for candidate HPV assays.
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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 | 0.021 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.006 | 0.001 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".