p16 expression and its correlation with the clinical pathological characteristics of patients with cervical cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: Overexpression of p16 has been documented in a variety of human tumours. Nonetheless, the association between p16 overexpression and the clinicopathological characteristics of patients with cervical cancer remains a subject of debate. This meta-analysis sought to systematically assess the relationship between p16 expression and the clinicopathological features of patients with cervical cancer. DESIGN: Systematic review and meta-analysis. DATA SOURCES: The PubMed, Embase, Cochrane Library (Central), Web of Science (SCI Expanded), and Chinese databases (CNKI, VIP, Wanfang and CBM) were searched through 1 March 2024. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Case‒control studies examining the association between p16 expression and cervical cancer were analysed to evaluate whether p16 expression was correlated with the clinicopathological characteristics of patients with cervical cancer. DATA EXTRACTION AND SYNTHESIS: Two independent reviewers employed standardised methods to search, screen and code the included studies. The risk of bias was evaluated using the Cochrane Collaboration tools and the Newcastle-Ottawa Scale. Statistical analyses and data processing were conducted using Review Manager V.5.4, which included heterogeneity tests and sensitivity analyses. Additionally, STATA V.16.0 was used for further sensitivity analyses of the included studies, and publication bias was assessed using Begg's test. CONCLUSIONS: The p16 protein is strongly associated with the onset and progression of cervical cancer and serves as a valuable biomarker for its early detection and diagnosis. PROSPERO REGISTRATION NUMBER: CRD42024546241.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.037 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".