A Pooled Analysis to Compare the Clinical Characteristics of Human Papillomavirus–positive and -Negative Cervical Precancers
Bibliographic record
Abstract
Abstract Given that high-risk human papillomavirus (HPV) is the necessary cause of virtually all cervical cancer, the clinical meaning of HPV-negative cervical precancer is unknown. We, therefore, conducted a literature search in Ovid MEDLINE, PubMed Central, and Google Scholar to identify English-language studies in which (i) HPV-negative and -positive, histologically confirmed cervical intraepithelial neoplasia grade 2 or more severe diagnoses (CIN2+) were detected and (ii) summarized statistics or deidentified individual data were available to summarize proportions of biomarkers indicating risk of cancer. Nineteen studies including 3,089 (91.0%) HPV-positive and 307 (9.0%) HPV-negative CIN2+ were analyzed. HPV-positive CIN2+ (vs. HPV-negative CIN2+) was more likely to test positive for biomarkers linked to cancer risk: a study diagnosis of CIN3+ (vs. CIN2; 18 studies; 0.56 vs. 0.24; P < 0.001) preceding high-grade squamous intraepithelial lesion cytology (15 studies; 0.54 vs. 0.10; P < 0.001); and high-grade colposcopic impression (13 studies; 0.30 vs. 0.18; P = 0.03). HPV-negative CIN2+ was more likely to test positive for low-risk HPV genotypes than HPV-positive CIN2+ (P < 0.001). HPV-negative CIN2+ appears to have lower cancer risk than HPV-positive CIN2+. Clinical studies of human high-risk HPV testing for screening to prevent cervical cancer may refer samples of HPV test–negative women for disease ascertainment to correct verification bias in the estimates of clinical performance. However, verification bias adjustment of the clinical performance of HPV testing may overcorrect/underestimate its clinical performance to detect truly precancerous abnormalities.
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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 | 0.022 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.038 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".