Time trends incidence of both major histologic types of esophageal carcinomas in selected countries, 1973–1995
Bibliographic record
Abstract
The purpose of our study was to examine the incidence patterns of 2 major histologic types of esophageal cancer, in selected countries world-wide and to identify components of birth cohort, period and age as determinants of observed time trends using regression modeling. The roles of temporal changes in specification of histology of tumors and of classification of cancers at the gastroesophageal junction as esophageal or gastric in origin were taken into consideration. In all, 56,426 esophageal cancer cases were included. The results indicate that the incidence rate of squamous cell carcinoma of the esophagus has been relatively stable in most of the countries analyzed, although increasing trends were observed in Denmark and the Netherlands (Eindhoven) among men and in Canada, Scotland and Switzerland among women. There was a significant increase in the incidence of esophageal adenocarcinomas in both sexes in the United States (among whites and blacks), Canada and South Australia and in 6 European countries (Scotland, Denmark, Iceland, Finland, Sweden and Norway). In France the increase was limited to men and in Switzerland the increase was observed only in women. Modeling was unable to distinguish which trends were the results of changes in risk between generations (as cohort effects), or changes in all age groups simultaneously (as a period effect).
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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 | 0.000 |
| 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".