Clinical Efficacy of Ursodeoxycholic Acid in Bile Reflux Gastritis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Bile reflux gastritis (BRG) may lead to precancerous lesions of gastric cancer and gastric cancer. Although ursodeoxycholic acid (UDCA) has been used to treat BRG, its clinical efficacy remains unknown. Therefore, we systematically evaluated the efficacy and safety of UDCA compared with conventional therapy for BRG. METHODS: We selected candidate studies and generated a forest plot to evaluate outcomes. Metaregression analysis was conducted to identify possible explanations for heterogeneity. Study quality was evaluated using the Cochrane Risk of Bias 2 tool and the Newcastle-Ottawa scale. The quality of evidence for the outcomes of the meta-analysis was assessed using the Grading of Recommendations Assessment, Development and Evaluation approach. RESULTS: A total of 14 studies including 1605 patients were identified. Compared with control groups, medication combined with UDCA significantly reduced the number of reflux episodes [mean difference (MD) = - 17.99 times, 95% CI (- 19.84, - 16.14)], shortened the longest duration of reflux [MD = - 9.21 min, 95% CI (- 12.63, - 5.80)], decreased the number of long reflux episodes [MD = - 3.21 times, 95% CI (- 3.76, - 2.66)], increased the clinical response rate [risk ratio = 1.15, 95% CI (1.10, 1.19)], decreased the gastrin content [MD = - 18.03 ng/L, 95% CI (- 33.88, - 2.18)] and alleviated symptoms. CONCLUSIONS: UDCA demonstrates significant therapeutic efficacy for BRG, particularly with 8-week regimens, supporting its potential as a first-line therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.043 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".