Continued increasing incidence of malignant appendiceal tumors in Canada and the United States: A population‐based study
Bibliographic record
Abstract
BACKGROUND: To the authors' knowledge, there are limited data regarding the epidemiology of malignant appendiceal tumors. It remains unknown whether the previously reported trends are occurring in different countries and/or continuing in recent years and/or whether they are possibly due to increasing rates of appendectomies. In the current study, the authors investigated the patterns and time trends of malignant appendiceal tumor diagnosis by age group, sex, stage of disease, and histology in Canada and the United States and concomitant rates of appendectomies in Canada. METHODS: The Canadian Cancer Registry and the US Surveillance, Epidemiology, and End Results incidence databases were used to identify incident patients of malignant appendiceal tumors in the 2 countries between 1992 and 2016. The Canadian national hospitals Discharge Abstract Database was used to identify appendectomies performed between 2004 and 2015. Joinpoint regression analyses were performed to determine time trends. RESULTS: There was an overall increase in the incidence of malignant appendiceal tumors of 232% in the United States and 292% in Canada between 2000 and 2016. The increase was noted for malignant adenocarcinomas and neuroendocrine appendiceal tumors in both countries. The increase occurred across all age groups, sexes, and stages of disease. The highest rate of increase was noted for appendiceal neuroendocrine malignant tumors diagnosed among the youngest age groups. The rate of appendectomies was stable in the recent time periods, resulting in a decreasing rate of appendectomies per malignant appendiceal tumor diagnosis. CONCLUSIONS: The incidence of malignant appendiceal tumor is continuing to increase, which is not likely due to the increasing diagnosis of asymptomatic tumors at the time of appendectomies.
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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 | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".