<scp>EUS</scp>‐guided coil and glue injection versus endoscopic glue injection for gastric varices: International multicentre propensity‐matched analysis
Bibliographic record
Abstract
BACKGROUND: Gastric varices (GVs) are conventionally managed with endoscopic cyanoacrylate (E-CYA) glue injection. Endoscopic ultrasound (EUS)-guided therapy using combination of coils and CYA glue (EUS-CG) is a relatively recent modality. There is limited data comparing the two techniques. METHODOLOGY: This international multicentre study included patients with GV undergoing endotherapy from two Indian and two Italian tertiary care centres. Patients undergoing EUS-CG were compared with propensity-matched E-CYA cases from a cohort of 218 patients. Procedural details such as amount of glue, number of coils used, number of sessions required for obliteration, bleeding after index procedure rates and need for re-intervention were noted. RESULTS: Of 276 patients, 58 (male 42, 72.4%; mean age-44.3 ± 12.1 years) underwent EUS-CG and were compared with 118 propensity-matched cases of E-CYA. In the EUS-CG arm, complete obliteration at 4 weeks was noted in 54 (93.1%) cases. Compared to the E-CYA cohort, EUS-CG arm showed significantly lower number of session (1.0 vs. 1.5; p < 0.0001) requirement, lower subsequent-bleeding episodes (13.8% vs. 39.1%; p < 0.0001) and lower re-intervention (12.1% vs. 50.4%; p < 0.001) rates. On multivariable regression analysis, size of the varix (aOR-1.17; CI 1.08-1.26) and technique of therapy (aOR-14.71; CI 4.32-50.0) were significant predictors of re-bleeding. A maximum GV size >17.5 mm had a 69% predictive accuracy for need for re-intervention. CONCLUSION: Endoscopic ultrasound-guided therapy of GV using coil and CYA glue is a safe technique with better efficacy and lower re-bleeding rates on follow-up compared to the conventional endoscopic CYA therapy.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".