Estimating global prevalence of gallbladder stones in general population from 2000 to 2024: systematic review and meta-analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 <i>via</i> 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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.072 | 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 it