A systematic review and meta-analysis of studies evaluating the performance of point-of-care tests for human papillomavirus screening
Bibliographic record
Abstract
Background High-risk human papillomavirus (HPV) is a necessary cause of high-grade cervical intraepithelial neoplasia (grade 2 or higher, CIN2+). Simplified and rapid HPV DNA assays designed for use in resource-limited settings have recently become available. Methods We performed a systematic review and meta-analysis by searching Medline, Embase, Global Health and CINAHL databases for studies from 1 January 2004 to 25 February 2017 that reported the performance of careHPV or OncoE6 for the detection of histological CIN2+ in cervical cancer screening. We used bivariate models to estimate pooled sensitivity and specificity for CIN2+ and CIN3+. Results A total of 29 657 women were included from seven studies evaluating the performance of careHPV for the detection of CIN2+ and four studies among 27 845 women for the detection of CIN3+. The pooled prevalence for CIN2+ and CIN3+ was 2.3% and 1.1%, respectively. careHPV had sensitivity and specificity of 88.1% (95% CI 81.4 to 92.7) and 83.7% (95% CI 74.9 to 89.8), respectively, for CIN2+ and 90.3% (95% CI 83.4 to 94.5) and 85.3% (95% CI 73.1 to 92.5), respectively, for CIN3+, using clinician-collected cervical specimen. The corresponding pooled estimates using self-collected vaginal swabs were 73.6% (95% CI 64.9 to 80.8) and 88.0% (95% CI 79.1 to 93.5) for CIN2+ and 75.2% (95% CI 66.8 to 82.0) and 90.6% (95% CI 83.4 to 94.9) for CIN3+. Two studies using OncoE6 reported sensitivity and specificity ranging from 31.3% to 42.4% and 99.1%–99.4% for CIN2+, and 53.5% and 98.9% for CIN3+ for one study. Conclusion CareHPV has good sensitivity and specificity for the detection of CIN2+ and CIN3+, but sensitivity was lower using self-collected vaginal samples. The specificity is lower in high HPV prevalence populations such as women living with HIV. OncoE6 assay warrants further evaluation.
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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 | 0.023 | 0.071 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.041 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".