Efficacy of Gastric Electrical Stimulation for Gastroparesis: US/European Comparison
Bibliographic record
Abstract
BACKGROUND: Gastric electrical stimulation (GES) is used in both the US and Europe, but little research has investigated the demographics of gastroparesis patients receiving GES by geographic location. METHODS: We compared data from 380 patients, 296 female and 84 males, mean age 42 years, 246 idiopathic (ID), 107 diabetic (DM), and 27 post-surgical (PS). The statistical significance was calculated by Chi-square test and a P-value obtained for ID, DM, and PS. The statistical significance was calculated by Fischer exact test and a P-value obtained comparing male vs. female. RESULTS: European centers had 61 GES patients compared to 319 from the US. In Europe, 100% of patients had gastric emptying test (GET) values available; in the US, it was 75% of patients. European centers had more DM patients (59%) than the US (22%), and a smaller proportion of ID patients (25%) than the US (72%). There was a statistical difference between the causes of gastroparesis in the patients receiving GES (P-value < 0.00001). There was also significant difference in the gender of the patients receiving GES, with a greater proportion of women in the US (P value = 0.0023). CONCLUSIONS: Comparing GES in US vs. Europe demonstrated significant differences in gastroparesis demographics and percentage of patients with GET data. After analyzing the previously discussed results and reviewing recent updates in evidence-based medicine guidelines, the discrepancy and variance in patient populations in the US and Europe emphasizes the need for a database that allows better analysis and treatment of gastroparesis patients worldwide including stimulation therapies.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".