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
Why this work is in the frame
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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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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 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 it