<p>Time Trends And Age-Period-Cohort Effects On The Incidence Of Gastric Cancer In Changle From 2003 To 2012</p>
Bibliographic record
Abstract
PURPOSE: Although the incidence of gastric cancer in China has declined over the past decades, they were still much higher than the average of global. This aim of this study was to describe the trends and age-period-cohort effects on gastric cancer incidence from 2003 to 2012 in Changle and to explore the potential reason. MATERIALS AND METHODS: Data on patients with gastric cancer diagnosed between 2003 and 2012 were collected by the population-based Changle cancer registration (n=4111). Age-standardized incidence rates of gastric cancer were calculated and joinpoint regression was used to evaluate the trends of gastric cancer incidence. Time trends in gastric cancer incidence by the period of diagnosis and birth cohort were analyzed by sex. Age-period-cohort analysis was performed to investigate the independent effects of age, period of diagnosis and birth cohort among over 25-year-old residents. RESULTS: A steady downward trend was observed among men, with the incidence ranging from 96.15 per 100,000 in 2003 to 62.6 per 100,000 in 2012 (APC, -5.1%; 95% CI: -6.9 to -3.2%). A similarly declining trend was observed among women with the incidence ranging from 34.5 per 100,000 to 15.7 per 100,000 (APC, -5.7%; 95% CI: -9.3 to -2.0%). Age-period-cohort model of incidence rate showed increasing age effect and decreasing period of diagnosis effects in both men and women. Birth cohorts exhibited a decreasing trend in the incidence among women who were born after 1935 and men after 1940. CONCLUSION: Recent decreases in the incidence of gastric cancer were due to decreased period of diagnosis and cohort effects, which was attributed to the improvements in their lifestyle and habits.
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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 | 0.002 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".