Estimating global prevalence of gallbladder stones in general population from 2000 to 2024: systematic review and meta-analysis
Bibliographic record
Abstract
Gallbladder stones (GS), is one of the most common and costly of all the gastrointestinal diseases. However, global prevalence estimates of GS remain heterogeneous due to methodological variations across studies, and consensus on risk factor hierarchies is still evolving. Therefore, we performed current study in order to estimate the global prevalence of GS. The quality of included studies was assessed using the Newcastle-Ottawa Scale. Data were analysed via the DerSimonian-Laird random-effects model with Logit transformations, and sensitivity analysis was performed using a ‘Leave-one-out’ approach. Of 18,277 identified records, 139 studies were included in the final analysis. The overall global prevalence of GS in the general population was 5.86% (95% CI 5.28–6.47). Marked geographical disparities were observed, with the highest prevalence in Uganda (21.92%, 95% CI 18.43–25.61) and the lowest in Australia (0.18%, 95% CI 0.17–0.18) – a 122-fold difference. Multivariable meta-regression showed that study size was the strongest predictor (importance: 97.79%). Regarding risk factors, female gender, age > 50 years, increased body mass index, and family history of GS were significantly associated with higher GS prevalence. In contrast, factors such as education level, smoking, alcohol consumption, lifestyle, vegetarian diet, and serum lipid levels had no significant impact. Comorbidities including hypertension, diabetes mellitus, and metabolic-associated fatty liver disease (MAFLD) were strongly correlated with elevated GS prevalence. This meta-analysis showed that the GS was a common disease and affected the health of one in twenty people worldwide. Accurate estimates of the global and population-based prevalence of GS are helpful for healthcare improvements.
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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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.041 |
| Bibliometrics | 0.011 | 0.011 |
| 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".