High‐risk HPV testing on self‐sampled <i>versus</i> clinician‐collected specimens: A review on the clinical accuracy and impact on population attendance in cervical cancer screening
Bibliographic record
Abstract
This review elaborates on the accuracy and feasibility of human papillomavirus (HPV) self-sampling, i.e., offering self-sampling of (cervico-)vaginal cell material by women themselves in nonclinical settings for high-risk HPV (hrHPV) detection in the laboratory, for cervical screening. To that end a bibliographic database search (PubMed) was performed to identify studies (published between January 1992 and January 2012) that compared clinical accuracy of HPV testing on self-sampled material with that of cytology or HPV testing on clinician-taken samples, and studies comparing response to offering HPV self-sampling with a recall invitation. Overall, hrHPV testing on self-samples appeared to be at least as, if not more, sensitive for cervical intraepithelial neoplasia grade 2 or worse (CIN2+) as cytology on clinician-obtained cervical samples, though often less specific. In most studies, hrHPV testing on self- and clinician-sampled specimens is similarly accurate with respect to CIN2+ detection. Variations in clinical performance likely reflect the use of different combinations of collection devices and HPV tests. Because it is known that underscreened women are at increased risk of cervical cancer, targeting non-attendees of the screening program could improve the effectiveness of cervical screening. In developed countries offering self-sampling has shown to be superior to a recall invitation for cytology in re-attracting original non-attendees into the screening program. Additionally, self-testing has shown to facilitate access to cervical screening for women in low resource areas. This updated review of the literature suggests that HPV self-sampling could be an additional strategy that can improve screening performance compared to current cytology-based call-recall programs.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".