Accuracy of liver and spleen stiffness on magnetic resonance elastography for detecting portal hypertension: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: The purpose of this systematic review and meta-analysis was to evaluate the diagnostic accuracy of liver and spleen stiffness on magnetic resonance elastography (MRE) for detecting clinically significant portal hypertension. METHODS: A systematic review of MEDLINE, EMBASE, Scopus, the Cochrane Library, and the Grey Literature through to 15 August 2019 was performed. Original articles with >10 patients evaluating liver and/or spleen stiffness on MRE using a reference standard of portal hypertension defined as intractable ascites, esophageal varices, encephalopathy and/or death were included in analysis. Patient, clinical, MRI, and diagnostic performance was independently acquired by two reviewers. Meta-analysis was performed using a bivariate mixed-effects regression model. RESULTS: Fourteen studies were included with 12 studies evaluating liver stiffness and eight studies evaluating spleen stiffness. The pooled and weighted sensitivity, specificity, and area under the curve (AUC) values for liver stiffness on MRE were 83% [95% confidence interval (CI) 72-90%], 80% (95% CI 70-88%), and 88% (95% CI 85-91%), respectively. The pooled and weighted sensitivity, specificity, and AUC values for spleen stiffness on MRE were 79% (95% CI 61-90%), 90% (95% CI 80-95%), and 92% (95% CI 89-94%), respectively. The liver and spleen stiffness sensitivity and specificity values were comparable when evaluating for esophageal varices only at of 80% (95% CI 66-89%) and 76% (95% CI 62-86%) for liver stiffness, and 75% (95% CI 52-90%) and 89% (95% CI 70-96%) for spleen stiffness. DISCUSSION: Liver and spleen stiffness on MRE can serve as a supplemental noninvasive assessment tools for detecting clinically significant portal hypertension. Spleen stiffness may be more specific and accurate than liver stiffness for detecting portal hypertension.
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.
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 | 0.022 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.037 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".