Comparison of ThinPrep and SurePath liquid‐based cytology and subsequent human papillomavirus DNA testing in China
Bibliographic record
Abstract
BACKGROUND: Liquid-based cytology (LBC) has been compared with conventional cytology in numerous studies. In the current study of 2 LBC systems, the accuracy, rates of unsatisfactory cytology, and sufficiency of residual LBC specimens for Hybrid Capture 2 (HC2) HPV DNA testing were compared. METHODS: Eligible women ages 30 to 49 years were recruited for this cross-sectional population-based study in rural China. Women were assessed by visual inspection with acetic acid (VIA), LBC, and high-risk HPV HC2 DNA assay. Cervical specimens were preserved according to SurePath or ThinPrep protocols. LBC results were manually read. HC2 testing was performed on specimens with sufficient residual volume. Colposcopies and biopsies were performed on women who were VIA positive at the time of initial screening. Women with abnormal LBC or HC2 test results were called back for colposcopies and 4-quadrant cervical biopsies. RESULTS: Of 2005 eligible women, 972 were tested by SurePath and 1033 by ThinPrep. Compared with SurePath samples, ThinPrep samples had higher rates of unsatisfactory cytology (0.2% for SurePath and 1.5% for ThinPrep) and insufficient residual volume for HC2 (0.0% for SurePath and 18.2% for ThinPrep). SurePath samples yielded higher sensitivities and similar specificities for LBC and HC2 testing of residual specimens, but these differences were not determined to be significant by area-under-the-curve analysis (LBC performance: 0.89 for SurePath and 0.85 for ThinPrep; HC2 performance: 0.91 for SurePath and 0.89 for ThinPrep). CONCLUSIONS: Both methods yielded similar validity in detecting significant cervical lesions. However, SurePath samples yielded higher rates of satisfactory LBC slides and sufficient residual volume for HC2.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".