A204 ENDOSCOPIC ULTRASOUND-GUIDED SCLEROTHERAPY VERSUS CONVENTIONAL SCLEROTHERAPY IN REDUCING RISK OF FUTURE GASTROESOPHAGEAL VARICEAL BLEED: SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
The use of ultrasound in endoscopy allows more precise delivery of sclerosant to observed varices. It theoretically allows one to target the vein feeding the varices, obliterate its origin and prevent the redevelopment of future varices at that site. We aimed to assess the efficacy of endoscopic ultrasound (EUS)-guided sclerotherapy in comparison to conventional sclerotherapy (via gastroscopy) in preventing future gastroesophageal variceal bleeds. A systematic review was performed using MEDLINE, EMBASE and Cochrane databases from inception to May 2018. The primary outcome was variceal bleed event rate. Secondary outcomes were all-cause gastrointestinal bleed event rate, percentage of patients with successful variceal eradication after first treatment, and adverse event rate. Two independent reviewers extracted data. Mantel-Haenszel random effect model meta-analysis was used whenever methodologically appropriate. Five out of 3,257 identified studies met the inclusion criteria. 2 more studies were excluded due to incomplete data. Studies were performed between 2014 and 2017. EUS-guided sclerotherapy was significantly associated with lower variceal-related bleeds [RR 0.34 (95% CI 0.14 to 0.82, p = 0.02)], as well as lower all-cause gastrointestinal bleed [RR 0.41 (95% CI 0.26 to 0.64; p<0.0001)]. There was no difference in the percentage of patients with successful variceal obliteration after first treatment [RR 1.05, 95% CI 0.90 to 1.23, (p=0.53)]. There was no statistical heterogeneity in any of the analyses (p= Non-significant, I2=0). There was no significant difference in adverse events between the two modalities. EUS-guided sclerotherapy significantly decreases the risk of variceal-related bleeds and all-cause gastrointestinal bleeds and should be considered when available. None
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.034 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".