Human Papillomavirus Viral Load as Triage Biomarker for High‐Grade Cervical Lesions and Invasive Cervical Carcinoma: A Cross‐Sectional Study
Bibliographic record
Abstract
ABSTRACT Background and Aims We herein evaluated whether intra‐tissue HPV viral load may constitute a triage biomarker to differentiate between high‐grade precancerous cervical lesions from intra cervical cancer (ICC). Methods 50 biopsy samples prospectively obtained from women living in Mauritania suffering from high‐grade cervical intraepithelial neoplasia (CIN2/3), adenocarcinoma (ADC) or squamous cell carcinoma (SCC) were analysed for HPV genotyping and quantitation carried out using Bioperfectus Multiplex Real Time Human Papillomavirus Genotyping Real Time PCR assay. Results HPV‐positive results were detected in 47 biopsies (12 CIN2/3 and 35 ICC, including 4 ADC and 31 SCC). The cumulative HPV viral loads of any HPV and high risk‐HPV (HR‐HPV) in ICC were significantly higher than those in CIN2/3 ( p < 0.002 for any HPV; 0.02 for HR‐HPV). The cumulative viral loads of any HPV and HR‐HPV possessed a good discriminatory ability to differentiate between CIN2/3 and ICC, with optimal cutoffs ranging from 4.38 (any HPV) to 4.85 (HR‐HPV) copies per 10,000 cells. Conclusion Our observations show that cumulative HPV viral load in cervical tissue may constitute a relevant biomarker associated with the severity of HPV‐related cervical lesions. HPV viral load in cervical tissue could be used as a triage tool for aggressive ICC in advanced cervical lesions.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".