Circulating microRNAs for Early Diagnosis of Ovarian Cancer: A Systematic Review and Meta-Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this study, we conducted a systematic review and meta-analysis to summarize and evaluate the global research potential of different circulating miRNAs as an early diagnostic biomarker for OC. A systematic literature search for relevant studies was conducted in June 2020 and followed up in November 2021. The search was conducted in English databases (PubMed, ScienceDirect). The primary search resulted in a total of 1887 articles, which were screened according to the prior established inclusion and exclusion criteria. We identified 44 relevant studies, of which 22 were eligible for the quantitative meta-analysis. Statistical analysis was performed using the Meta-package in Rstudio. Standardized mean differences (SMD) of relative levels between control subjects and OC patients were used to evaluate the differential expression. All studies were quality evaluated using a Newcastle-Ottawa Scale. Based on the meta-analysis, nine miRNAs were identified as dysregulated in OC patients compared to controls. Nine were upregulated in OC patients compared to controls (miR-21, -125, -141, -145, -205, -328, -200a, -200b, -200c). Furthermore, miR-26, -93, -106 and -200a were analyzed, but did not present an overall significant difference between OC patients and controls. These observations should be considered when performing future studies of circulating miRNAs in relation to OC: sufficient size of clinical cohorts, development of consensus guidelines for circulating miRNA measurements, and coverage of previously reported miRNAs.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it