Bibliographic record
Abstract
Cervical cancer remains a concern worldwide, and cervical cancer screening plays an important role in reducing the burden of this disease. Although cytology is still the main strategy for cervical cancer screening, it has gradually changed to human papillomavirus testing. The specificity of human papillomavirus testing is lower than that of cytology, which leads to an increased rate of colposcopy after positive results. To decrease colposcopic examinations, an efficient triage method is needed for human papillomavirus screening. New biomarkers have been developed and evaluated for primary screening and triage of abnormal cytology or human papillomavirus-positive results. Their sensitivity and specificity were estimated and compared with those of cytology. In the present study, the following new techniques were examined: p16/Ki67 dual staining, DNA methylation, micro-ribonucleic acid, chromosomal abnormalities, Claudins and DNA ploidy. Evaluation studies of p16/Ki67 dual staining and DNA methylation were more advanced than those of other options. When p16/Ki67 dual staining was used for triage for human papillomavirus testing, the sensitivity of 2 or greater cervical intraepithelial neoplasia (CIN2+) detection was higher than that of cytology without decreased specificity. Although there are several types of DNA methylation, sensitivity and specificity were moderate for detecting CIN2+. S5 classifier is a commercialized product that consists of viral methylation, and high sensitivity with decreased specificity has been reported. Considering its combination with self-sampling, DNA methylation is a highly anticipated technique along with human papillomavirus testing for the next generation of cervical cancer screening. However, the backgrounds for cervical cancer screening differ among countries and further study is needed to identify the best available method.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.012 |
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".