Circulating MicroRNAs as Potential Diagnostic Biomarkers for Cervical Intraepithelial Neoplasia and early Cervical Cancer : A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Review question / Objective: Cervical cancer is the predominant form of malignancy affecting the female reproductive system, with cervical intraepithelial neoplasia (CIN) serving as its precursor lesion, capable of advancing to invasive cervical cancer (CC).Despite limited investigations examining circulating microRNAs (miRNAs) as potential diagnostic biomarkers for CIN and early-CC, the results have been conflicting.In light of these discrepancies, this metaanalysis was undertaken to assess the diagnostic accuracy of miRNAs for CIN and early-CC, and identify possible sources of heterogeneity across studies.Eligibility criteria: The inclusion criteria were as follows: (1) studies must be relevant to the diagnostic performance of circulating miRNAs for CIN or CC diagnosis; (2) the case group must consist of patients who were diagnosed using clinically recognized diagnostic criteria; and (3) the frequencies of false positive (FP), true positive (TP), false negative (FN), and true negative (TN) could be extracted directly or indirectly from the studies.By contrast, studies meeting any of the following exclusion criteria were excluded: (1) cell, animal, or microbiological trials; (2) non-case-control studies; and (3) reviews, meta-analyses, or conference abstracts.INPLASY registration number: This protocol was registered with the International Platform of Registered Systematic Review and Meta-Analysis Protocols (INPLASY) on 17 April 2023 and was last u p d a t e d o n 1 7 A p r i l 2 0 2 3 ( r e g i s t r a t i o n n u m b e r INPLASY202340053).
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.018 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".