A Pooled Analysis to Compare the Clinical Characteristics of Human Papillomavirus–positive and -Negative Cervical Precancers
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it