Abstract WMP70: Redefining ESUS Evaluation: The Role of Pelvic MRV – A Scoping Review and Meta-Analysis
Bibliographic record
Abstract
Background: There is 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 a Patent Foramen Ovale (PFO). Pelvic DVT is thought to occur uncommonly, however studies have shown that around 20% of patients can have isolated pelvic vein DVTs without evidence of lower extremity DVTs. We aimed to describe diagnostic yield of pelvic MRV in ESUS patients. Review summary: A systemic search was carried out using PubMed following PRISMA guidelines. We retrieved 6 cross sectional and cohort studies, 2 case series, as well as 9 case reports with a total of 1319 patients and a mean age of 51 years. 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 two reports, which used pelvic CTV. Conclusion: Pelvic venous abnormalities are relatively common findings in ESUS patients with a PFO and negative lower extremity DVT. Pelvic MRV should be highly considered in these situations as finding pelvic vein DVT would change the management in these patients as they will need anticoagulation. Future research should strive to provide clear guidance on clinical decision making and cost effectiveness of utilizing this valuable tool using highly controlled, 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 | 0.018 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.019 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".