The APTIMA HPV assay <i>versus</i> the hybrid capture 2 test in triage of women with ASC‐US or LSIL cervical cytology: A meta‐analysis of the diagnostic accuracy
Bibliographic record
Abstract
Testing for DNA of 13 high-risk HPV types with the Hybrid Capture 2 (HC2) test has consistently been shown to perform better in triage of women with cervical cytology results showing atypical squamous cells of undetermined significance (ASC-US) but often not in triage of low-grade squamous intraepithelial lesions (LSIL) detected in cervical cancer screening. In a meta-analysis, we compared the accuracy of the APTIMA HPV test, which identifies RNA of 14 high-risk HPV types, to HC2 for the triage of women with ASC-US or LSIL. Literature search-targeted studies where the accuracy of APTIMA HPV and HC2 for detection of underlying CIN2/3+ was assessed concomitantly including verification of all cases of ASC-US and LSIL. HSROC (Hierarchical Summary ROC) curve regression was used to compute the pooled absolute and relative sensitivity and specificity. Eight studies, comprising 1,839 ASC-US and 1,887 LSIL cases, were retrieved. The pooled sensitivity and specificity of APTIMA to triage ASC-US to detect underlying CIN3 or worse was 96.2% (95% CI = 91.7-98.3%) and 54.9% (95% CI = 43.5-65.9%), respectively. APTIMA and HC2 showed similar pooled sensitivity; however, the specificity of the former was significantly higher (ratio: 1.19; 95% CI = 1.08-1.31 for CIN2+). The pooled sensitivity and specificity of APTIMA to triage LSIL were 96.7% (95% CI = 91.4-98.9%) and 38.7% (95% CI = 30.5-47.6%) for CIN3+. APTIMA was as sensitive as HC2 but more specific (ratio: 1.35; 95% CI = 1.11-1.66). Results were similar for detection of CIN2 or worse. In both triage of ASC-US and LSIL, APTIMA is as sensitive but more specific than HC2 for detecting cervical precancer.
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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 | 0.019 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.047 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".