Incidence and Survival for Gastric and Esophageal Cancer Diagnosed in British Columbia, 1990 to 1999
Bibliographic record
Abstract
BACKGROUND: Geographical variation and temporal trends in the incidence of esophageal and gastric cancers vary according to both tumour morphology and organ subsite. Both diseases are among the deadliest forms of cancer. The incidence and survival rates for gastric and esophageal carcinoma in British Columbia (BC) between 1990 and 1999 are described. METHODS: Incidence data for the period 1990 to 1999 were obtained from the BC Cancer Registry. Age-adjusted incidence and survival rates were computed by anatomical subsite, histological type and sex. All rates were standardized to the 1996 Canadian population. The estimated annual percentage change (EAPC) was used to measure incidence changes over time. Kaplan-Meier curves were used to show survival rates, and log-rank tests were used to test for differences in the curves among various groups. RESULTS: Between 1990 and 1999, 1741 esophageal cancer cases and 3431 gastric cancer cases were registered in BC. There was an increase in the incidence of adenocarcinoma of the esophagus over time (EAPC=9.6%) among men, and of gastric cardia cancer among both women (EAPC=9.2%) and men (EAPC=3.8%). Patients with proximal gastric (cardia) cancer had significantly better survival rates than patients with cancer in the lower one-third of the esophagus. Among gastric cancers, patients with distal tumours had a significantly better survival rate than patients with proximal tumours. DISCUSSION: The incidences of proximal gastric cancer and esophageal adenocarcinoma are increasing, and their survival patterns are different. Examining these cancers together may elucidate new etiological and prognostic factors.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".