Trends in incidence of anal cancer in Austria, 1983–2016
Bibliographic record
Abstract
BACKGROUND: Recent reports have noted increasing rates of anal cancer among high-income countries worldwide; however, little is known about these trends in Austria. METHODS: Data on anal cancer from 1983 to 2016 were obtained from Statistics Austria. All tumors (n = 3567) were classified into anal squamous cell carcinomas (ASCC), anal adenocarcinomas (AADC), and others (unspecified carcinoma and other specific carcinoma). Anal cancer incidence rates were calculated in 5‑year cycles and incidence average annual percentage change (AAPC) to evaluate trends by sex, histology and age group. RESULTS: The incidence rate of anal cancer was higher among females than males (relative risk, RR = 1.66, 95% confidence interval, CI: 1.55-1.79, p < 0.0001). From 1983 through 2016, incident anal cancer increased significantly (0.92 per 100,000 person-years to 1.85 per 100,000 person-years, AAPC = 1.93, 95% CI: 1.52 to 2.34, p < 0.0001), particularly among those 40-69 years old. From 1983 through 2016, the increasing anal cancer incidence was primarily driven by ASCC (0.47-1.20 per 100,000 person-years, AAPC = 2.23, 95% CI: 1.58 to 2.88, p < 0.0001) and others (other than ASCC and AADC, AAPC = 1.78, 95% CI: 1.01-2.55), yet stable in AADC (AAPC = 0.88, 95% CI: -0.48-2.25). CONCLUSIONS: Despite being a rare cancer in Austria, the increase in anal cancer incidence rate from 1983 to 2016 was substantial, particularly in ASCC. The observed rising trends reflect the need to investigate associated risk factors that have increased over time to inform preventive measures.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
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".