HPV Testing of Self-Collected Vaginal Swabs for Cervical Cancer Prevention in South Africa
Bibliographic record
Abstract
Abstract 33 Background: Cervical cancer screening programs in Low and Middle Income Countries (LMIC) need to be strengthened. One of the challenges is insufficient health care personnel to achieve optimal coverage. We evaluated the potential of human papillomavirus (HPV) testing using self-collected vaginal swabs to improve screening. Methods: As part of a larger NCI-supported study in Cape Town, South Africa, we recruited 261 HIV-uninfected and 237 HIV-infected women aged 30-60 years, at one primary health care site. All women were instructed to self-collect a vaginal swab on site immediately prior to a gynecologic exam, during which a cervical sample was collected. Both self- and clinician-collected samples were tested for high risk HPV types (16, 18, 45, 31, 33, 35, 52, 58, 51, 59, 39, 56, 66, 68) using Cepheid GeneXpert (HPV XpertTM). All women underwent at least one colposcopy with histological sampling. Classification of endpoint was based on expert pathology review. Results: The HPV prevalence using the cervical vs. self sample, respectively, was 14% vs. 25% among HIV-uninfected and 50% vs. 62% among HIV-infected women. Among women who were diagnosed with grade 2 or 3 cervical intraepithelial neoplasia (CIN) or cervical cancer, 94% were positive on HPV Xpert on the cervical and 88% on the vaginal swab, ignoring HIV status. However, specificity was poor for HPV tests done on self samples, 78% in HIV-uninfected and 52% in HIV-infected women. On a patient preference questionnaire, >90% of women stated they would be prepared to collect a sample at home and two-thirds expressed a preference to be examined by a clinician. Conclusion: Even in self-collected vaginal samples, HPV Xpert has excellent sensitivity for detecting cervical cancer precursor lesions. In LMIC settings where the ratio of health care workers to the population is low, HPV testing of self-collected samples could successfully triage women who require further evaluation and treatment. AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST: Louise Kuhn No relationship to disclose Rakiya Saidu No relationship to disclose Cecilia Svanholm-Barrie No relationship to disclose Ana Tergas Consulting or Advisory Role: Helomics Rosalind Boa No relationship to disclose Jennifer Moodley No relationship to disclose Thomas C. Wright No relationship to disclose David Persing No relationship to disclose Scott Campbell No relationship to disclose Lynette Denny No relationship to disclose
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".