Human Papillomavirus Detectability and Cervical Cancer Prognosis
Bibliographic record
Abstract
OBJECTIVE: To evaluate whether testing positive for human papillomavirus (HPV) before treatment is associated with cervical cancer recurrence and disease-free, cancer-specific, and overall survival and to report the relationship of HPV to cervical cancer histology, stage, grade, tumor size, lymph node involvement, and treatment response. DATA SOURCES: EMBASE and MEDLINE were searched from inception to January 27, 2022, with the use of MeSH terms and keywords relating to cervical cancer, HPV, and prognosis. ClinicalTrials.gov was not searched because of the nature of our review question. METHODS OF STUDY SELECTION: Studies must have assessed HPV DNA or RNA in cervical pretreatment biopsies or cells from 20 or more patients with invasive cervical cancer followed up for any length of time and reported the effect of testing positive or negative for HPV on cervical cancer recurrence, disease-free survival, cancer-specific survival, or overall survival. We extracted data on HPV-detection methods, patient and tumor characteristics, and clinical outcomes. TABULATION, INTEGRATION, AND RESULTS: Hazard ratios (HRs) and 95% CIs were pooled with a random-effects model. Meta-regression was performed to explore heterogeneity. Of 11,179 titles or abstracts and 474 full-text articles reviewed, 77 studies were included in the systematic review. Among these 77 studies, 30 reported on the relationship of HPV status to histology, 39 to cancer stage, 13 to tumor grade, 17 to tumor size, 23 to lymph node involvement, and four to treatment response. Testing positive for HPV was associated with better disease-free survival (HR 0.38, 95% CI 0.25-0.57; 15 studies with 2,564 cases), cancer-specific survival (HR 0.56, 95% CI 0.44-0.71; nine studies with 1,398 cases), and overall survival (HR 0.59, 95% CI 0.47-0.74; 36 studies with 9,169 cases), but not recurrence (HR 0.59, 95% CI 0.33-1.07; eight studies with 1,313 cases). Meta-regression revealed that the number of cases, tumor grade, specimen type, gene target, and HPV prevalence together explained 73.8% of the between-study heterogeneity. CONCLUSION: This review indicates that HPV detectability in cervical cancer is associated with a better clinical prognosis. SYSTEMATIC REVIEW REGISTRATION: https://osf.io/dtyeb .
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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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.011 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".