Predictive Value of HPV Testing in Self-collected and Clinician-Collected Samples Compared with Cytology in Detecting High-grade Cervical Lesions
Bibliographic record
Abstract
Abstract Background: Self-sampling has become an attractive proposition now that human papillomavirus (HPV) primary testing is being incorporated into cervical cancer screening programs worldwide. We compared predictive values of HPV testing based on self- and physician-collected samples, and cytology, in detecting high-grade cervical intraepithelial neoplasia (CIN). Methods: The Cervical And Self-Sample In Screening (CASSIS) study enrolled 1,217 women ages 16–70 years prior to scheduled colposcopies. Vaginal specimens were self-collected using the validated HerSwab device. Cervical specimens were collected by gynecologists. Specimens were tested for presence of high-risk HPV (hrHPV) by the Cobas 4800 HPV test. We estimated positive predictive values (PPV) and negative predictive values (NPV) and 95% confidence intervals (CI) for a subset of women (n = 700) who underwent cervical biopsy and cytology at the actual CASSIS visit. Results: hrHPV was detected in 329 women (47%) with HerSwab and in 327 (46.7%) with physician sampling. Respective values for HPV16/18 were 119 (17%) and 121 (17.3%). On histology, 134 women had CIN1, 49 had CIN2, 48 had CIN3, 5 had CIN2/CIN3, and 3 had cancers. PPVs for CIN2+ of any hrHPV were 28% (95% CI, 23.2–33.1) and 29.7% (95% CI, 24.8–34.9) for HerSwab and physician samples, respectively. Corresponding values for HPV16/18 were 43.7% (95% CI, 34.6–53.1) and 43.8% (95% CI, 34.8–53.1). PPV of cytology (ASC-US+) was 26.6% (95% CI, 21.6–32.0). Corresponding NPVs (same order as PPVs) were 96.4% (95% CI, 93.9–98.1), 97.8% (95% CI, 95.6–99), 90.9% (95% CI, 88.2–93.1), 91% (95% CI, 88.4–93.2), and 94.7% (95% CI, 91.8–96.8). Conclusions: Our results confirm that HPV self-sampling has comparable performance with a physician-collected sample in detecting cervical lesions. Impact: HPV self-sampling has the potential to increase coverage in cervical cancer screening.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".