Diagnostic accuracy of serum derived exosomes for hepatocellular carcinoma: 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
INTRODUCTION: Early and non-invasive detection of hepatocellular carcinoma (HCC), which is usually asymptomatic, can improve overall survival outcomes. The objective of this systematic review and meta-analysis was to evaluate the diagnostic accuracy of serum-derived exosomes for diagnosing HCC. METHODS: PubMed, Web of Science, and Scopus databases were searched for relevant studies up to April 2023. The quality of included studies was assessed using the QUADAS-2 checklist, and data were extracted. Statistical analysis was performed on 18 studies from 3,993 records, and a diagnostic meta-analysis was conducted. Biomarkers were categorized into four groups based on their type (exosomal miRNAs, exosomal RNAs, alpha-fetoprotein (AFP), and exosomal RNAs+AFP panel), and a meta-analysis was conducted for each category separately. RESULTS: The highest pooled sensitivity was 0.86 for exosomal miRNAs, and exosomal RNAs+AFP had the highest pooled specificity; (0.89). Furthermore, exosomal RNAs+AFP had the highest pooled positive likelihood ratio; (7.55), the highest pooled diagnostic odds ratio (35.96) and the highest pooled area under the curve (0.93). Exosomal miRNAs had the lowest pooled negative likelihood ratio; (0.17). CONCLUSIONS: The diagnostic accuracy of exosomal biomarkers is superior to that of AFP, and combining the two in a panel yields the better results.
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 | 0.001 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.007 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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