Trends in colorectal cancer survival in northern Denmark: 1985–2004
Bibliographic record
Abstract
OBJECTIVE: The prognosis for colorectal cancer (CRC) is less favourable in Denmark than in neighbouring countries. To improve cancer treatment in Denmark, a National Cancer Plan was proposed in 2000. We conducted this population-based study to monitor recent trends in CRC survival and mortality in four Danish counties. METHOD: We used hospital discharge registry data for the period January 1985-March 2004 in the counties of north Jutland, Ringkjøbing, Viborg and Aarhus. We computed crude survival and used Cox proportional hazards regression analysis to compare mortality over time, adjusted for age and gender. A total of 19,515 CRC patients were identified and linked with the Central Office of Civil Registration to ascertain survival through January 2005. RESULTS: From 1985 to 2004, 1-year and 5-year survival improved both for patients with colon and rectal cancer. From 1995-1999 to 2000-2004, overall 1-year survival of 65% for colon cancer did not improve, and some age groups experienced a decreasing 1-year survival probability. For rectal cancer, overall 1-year survival increased from 71% in 1995-1999 to 74% in 2000-2004. Using 1985-1989 as reference period, 30-day mortality did not decrease after implementation of the National Cancer Plan in 2000, neither for patients with colon nor rectal cancer. However, 1-year mortality for patients with rectal cancer did decline after its implementation. CONCLUSION: Survival and mortality from colon and rectal cancer improved before the National Cancer Plan was proposed; after its implementation, however, improvement has been observed for rectal cancer only.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".