Incidence and Prevalence of Primary Sclerosing Cholangitis: A Meta-analysis of Population-based Studies
Bibliographic record
Abstract
BACKGROUND: Primary sclerosing cholangitis is a chronic liver disease associated with significant morbidity, mortality, and healthcare utilization. We conducted a systematic review and meta-analysis of population-based studies of the incidence and prevalence of primary sclerosing cholangitis. METHODS: Medline and Embase were systematically searched to identify population-based studies of a defined geographic area and reported the incidence or prevalence of primary sclerosing cholangitis in the general population. Meta-analyses, using random-effects, were performed to calculate overall and country-specific incidence (per 100 000 persons/year) and prevalence rates (per 100 000 persons) with 95% confidence intervals. RESULTS: The 14 studies on incidence and the 12 for prevalence originated from North America, Asia, Europe, and Oceania. Incidence and prevalence rates of primary sclerosing cholangitis were 0.87 (95% confidence interval, 0.59-1.29) and 13.53 (95% confidence interval, 10.20-17.94) per 100 000 persons, respectively. CONCLUSIONS: Both the prevalence and incidence of primary sclerosing cholangitis is low in the general population. Future studies on the incidence and prevalence of primary sclerosing cholangitis in the general population should be directed at Asia, Africa, and Latin America to allow for a more robust assessment of the global epidemiology of primary sclerosing cholangitis.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.029 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".