A61 GLOBAL INCIDENCE OF APPENDICITIS: A POPULATION-BASED STUDY OF THE ORGANISATION FOR ECONOMIC CO-OPERATION AND DEVELOPMENT
Bibliographic record
Abstract
Abstract Background The global incidence of appendicitis in 2019 was estimated at 228 per 100,000 person-years. However, temporal trends of appendicitis rates vary between early industrialized and newly industrialized regions. Aims To analyze annual appendectomy rates in regions of the Organisation for Economic Co-operation and Development (OECD) in the 21st century. Methods We conducted an observational population-based cohort study using data from 34 OECD regions from 2000–2023. OECD data provides country-level annual hospitalization rates for appendectomy per 100,000 person-years. We used Poisson regression to calculate Average Annual Percentage Change (AAPC) in appendectomy rates, with associated 95% confidence intervals (CI) for each region. CIs crossing 0% were defined as stable. Results We observed geographic variation in appendectomy incidence rates, with rates ranging from 56.8 per 100,000 in Portugal (2023) to 165.6 per 100,000 in Switzerland (2022) (Table 1). Appendectomy rates significantly decreased in 22 regions and significantly increased in 10 regions, with AAPCs ranging from −4.25% (95%CI: −4.28, −4.22) in Italy to 1.46% (95%CI: 1.23, 1.68) in Norway (Table 1). AAPCs in Iceland were stable, and Latvia had insufficient data for time trend analysis. Conclusions In the 21st century, time trends of appendectomy rates across OECD regions displayed variation, with the majority decreasing. Geographic variability in rates and trends over time may be due to factors such as differential access to improved diagnostic imaging and non-surgical treatments. Table 1. The Average Annual Percentage Change (AAPC) in appendectomy for the 34 regions of the OECD dataset with the corresponding year ranges for each region, confidence intervals, and associated average incidence per 100,000 person-years. All region AAPCs are significantly increasing or decreasing except Iceland and Latvia. Funding Agencies None
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".