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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".