Diagnostic Yield of Pelvic MRV in Patients With Embolic Stroke of Undetermined Source (ESUS)
Bibliographic record
Abstract
BACKGROUND: There is a controversy in the literature regarding the role of pelvic venous abnormalities screening through Magnetic Resonance Venogram (MRV) in patients with Embolic Stroke of Undetermined Source (ESUS) and Patent Foramen Ovale (PFO). We aimed to describe diagnostic yield of pelvic MRV in ESUS patients. REVIEW SUMMARY: A systematic search was carried out using PubMed, ScienceDirect, and Google Scholar on the 5th of January of 2024, following PRISMA guidelines. We retrieved 6 cross-sectional and cohort studies, 2 case series, as well as 12 case reports with a total of 1321 patients and a mean age of 51 years. Only cross-sectional and cohort studies were included in the qualitative synthesis. The diagnostic yield of pelvic MRV in all included ESUS patients was 10% (95% CI: 8-12). In ESUS patients with a negative lower extremity DVT, the diagnostic yield was 9% (95% CI: 7-10). Patients with ESUS and PFO had significantly higher prevalence of abnormal pelvic MRV findings, OR=3.63 (95% CI: 1.53-8.61, P <0.01). All reviewed studies utilized pelvic MRV, except 4 reports, which used pelvic CTV and MRA. CONCLUSION: Pelvic venous abnormalities are relatively common findings in ESUS patients with a PFO and negative lower extremity DVT. Pelvic MRV can be considered in these situations. Future research should strive to provide clear guidance on clinical decision-making and cost-effectiveness of utilizing this valuable tool using randomized, controlled, and comparative studies.
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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 | 0.007 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".