Assessment of HPV screening modalities within primary care: a systematic review
Bibliographic record
Abstract
Introduction: Most cervical cancer precancerous lesions are associated with high-risk human papillomavirus (HPV) subtypes. Early detection through screening is crucial for preventing and managing HPV-related diseases. HPV Self-sample screening is a proposed method that can mitigate socioeconomic disparities, reduce embarrassment and costs of screening. This can possibly reduce the overall disease burden. Methods: A search strategy was conducted across multiple databases, including PubMed, Cochrane Library, Scopus, and Embase. Data extraction was performed using a standardized form to collect detailed information on study characteristics, participant demographics, and various outcomes. The quality and risk of bias in the articles were assessed using the Critical Appraisal skills programme (CASP) checklist, and the Cochrane Risk of Bias (ROB) tool. Results: Our review consistently found that HPV self-sampling is comparable to clinician-collected samples in terms of HPV detection rates and sensitivity, supporting the idea that HPV self-sampling can be a viable alternative for cervical cancer screening. Across the studies, self-sampling showed comparable or greater effectiveness to clinician-collected samples in detecting HPV in individuals. Specificity was comparable between both methods, with clinician-collected sampling slightly outperforming HPV self-sampling in some cases. Moreover when analyzing the negative predictive value (NPV) and positive predictive value (PPV) across the studies, it was evident that there was little difference between clinician-collected sampling and HPV self-sampling. 64.3% favored self-sampling over clinician-collected sampling due to increased comfort and privacy. Overall, the evidence suggests that self-sampling is an effective, patient-preferred, and cost-efficient alternative to clinician-collected sampling, particularly in under-screened populations.
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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 | 0.012 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".