The Global Incidence of Appendicitis
Bibliographic record
Abstract
OBJECTIVE: We compared the incidence of appendicitis or appendectomy across the world and evaluated temporal trends. SUMMARY BACKGROUND DATA: Population-based studies reported the incidence of appendicitis. METHODS: We searched MEDLINE and EMBASE databases for population-based studies reporting the incidence of appendicitis or appendectomy. Time trends were explored using Poisson regression and reported as annual percent change (APC) with 95% confidence intervals (CI). APC were stratified by time periods and pooled using random effects models. Incidence since 2000 was pooled for regions in the Western world. RESULTS: The search retrieved 10,247 citations with 120 studies reporting on the incidence of appendicitis or appendectomy. During the 21st century the pooled incidence of appendicitis or appendectomy (in per 100,000 person-years) was 100 (95% CI: 91, 110) in Northern America, and the estimated number of cases in 2015 was 378,614. The pooled incidence ranged from 105 in Eastern Europe to 151 in Western Europe. In Western countries, the incidence of appendectomy steadily decreased since 1990 (APC after 1989=-1.54; 95% CI: -2.22, -0.86), whereas the incidence of appendicitis stabilized (APC=-0.36; 95% CI: -0.97, 0.26) for both perforated (APC=0.95; 95% CI: -0.25, 2.17) and nonperforated appendicitis (APC=0.44; 95% CI: -0.84, 1.73). In the 21st century, the incidence of appendicitis or appendectomy is high in newly industrialized countries in Asia (South Korea pooled: 206), the Middle East (Turkey pooled: 160), and Southern America (Chile: 202). CONCLUSIONS: Appendicitis is a global disease. The incidence of appendicitis is stable in most Western countries. Data from newly industrialized countries is sparse, but suggests that appendicitis is rising rapidly.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".