Co-testing for detection of high-grade cervical intraepithelial neoplasia and cancer compared with cytology alone: a meta-analysis of randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: Human papillomavirus (HPV) DNA testing combined with cytology has been recommended as a primary cervical cancer screening strategy. METHODS: PubMed/MEDLINE, Embase, the Cochrane Library and the NIH trial registry were searched for randomized controlled trials comparing co-testing with cytology alone for the detection of high-grade CIN lesions and cancers. Of 1156 articles identified, four met inclusion criteria. The performance of co-testing and cytology alone was compared at baseline screening, second round screening and overall. Cumulative meta-analysis, Begg's test, Egger's test and sensitivity analysis were performed. RESULTS: At baseline, co-testing was associated with a significantly higher detection rate of CIN 2+ [risk ratio (RR) = 1.41, 95% confidence interval (CI): 1.12, 1.76] and a non-significantly higher CIN 3+ detection rate (RR = 1.15, 95% CI: 0.99, 1.33). At second round screening, co-testing was associated with significantly lower detection rates of both CIN 2+ and CIN 3+ (RR = 0.77, 95% CI: 0.63, 0.93; RR = 0·68, 95% CI: 0.55, 0.85). The overall detection rate did not differ between co-testing and cytology alone for CIN 2+ (RR: 1·19, 95% CI: 0.99, 1.46) or CIN3+ (RR: 0.99, 95% CI: 0.87, 1.14). CONCLUSION: Co-testing increases the detection of CIN2+ lesions at baseline and significantly decreases the detection rates of CIN2+ or CIN3+ lesions at subsequent screening compared with cytology alone.
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 | 0.027 | 0.057 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.066 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| 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 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".