Epidemiology, mortality and prevalence of colorectal cancer in ulcerative colitis patients between 2010-2016 in Hungary – a population-based study
Bibliographic record
Abstract
BACKGROUND: The incidence and prevalence of ulcerative colitis (UC) varies geographically. The risk of colorectal cancer (CRC) and possibly some other malignancies is increased among patients with UC. It is still debated if patients with UC are at a greater risk of dying compared with the general population. Our aim was to describe the epidemiology and mortality of the Hungarian UC population from 2010 to 2016 and to analyze the associated malignancies with a special focus on CRC. METHODS: This is an observational, descriptive, epidemiological study based on the National Health Insurance Fund social security databases from 2010 to 2016. All adult patients who had at least two events in outpatient care or at least two medication prescriptions, or at least one inpatient event with UC diagnosis were analyzed. Malignancies and CRC were defined using ICD-10 codes. We also evaluated the survival of patients suffering from UC compared with the general population using a 3 to 1 matched random sample (age, gender, geography) from the full population of Hungary. RESULTS: We found the annual prevalence of UC 0.24-0.34%. The incidence in 2015 was 21.7/100 000 inhabitants. Annual mortality rate was 0.019-0.023%. In this subpopulation, CRC was the most common cancer, followed by non-melanotic skin and prostate cancer. 8.5% of the UC incident subpopulation was diagnosed with CRC. 470 (33%) of the CRC patients died during the course of the study (25% of all deaths were due to CRC), the median survival was 9.6 years. UC patients had significantly worse survival than their matched controls (HR = 1.65, 95% CI: 1.56-1.75). SUMMARY: This is the first population-based study from Eastern Europe to estimate the different malignancies and mortality data amongst Hungarian ulcerative colitis patients. Our results revealed a significantly worse survival of patients suffering from UC compared to the general population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".