Impact of Acetic Acid on HPV Testing Using Hybrid Capture 2
Bibliographic record
Abstract
As per the American Society for Colposcopy and Cervical Pathology guidelines, human papillomavirus (HPV) testing is currently used as part of cervical cancer screening and during colposcopy follow-up. The present project evaluated if the application of acetic acid (AA) impacts HPV test results. METHODS: We conducted a prospective nonrandomized interventional study. Participants referred for colposcopy were eligible if immunocompetent, older than 18 years, and not pregnant. Women in group A (controls) received 2 consecutive HPV tests without application of AA. Women in group B had a first HPV sample collected before the application of AA and a second sample collected 3 minutes after application of AA. Samples were tested for HPV DNA with Hybrid Capture 2 (HC2) according to the manufacturer's instructions. RESULTS: From October 17, 2012, to January 10, 2013, approximately 101 women were recruited in 2 colposcopy clinics. In each group, concordance was 98%, with only 1 participant having discordant results (testing negative on the first sample and positive on the second sample). We found no statistically significant difference in relative light units(RLUs) between groups (median of difference, - 0.02 vs -0.05 RLU; p = .93). CONCLUSIONS: The results of this study suggest that acetic acid at concentrations of 3% to 5% and sequential cervical sampling do not modify the result of HPV testing by Hybrid Capture 2.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".