Age-period-cohort modeling of oesophageal carcinoma risk in a middle eastern country: 1980–2019
Bibliographic record
Abstract
BACKGROUND: Understanding of the factors influencing oesophageal cancer trends is crucial. Therefore, this cross-sectional cohort study sought to disentangle the age, period and cohort effects on the trends of oesophageal cancer in Kuwait. METHODS: The data on incident oesophageal carcinoma cases diagnosed between January 1, 1980, through December 31, 2019, and reference population were obtained. Age-period-cohort (APC) analysis was conducted using a loglinear Poisson regression model. RESULTS: A total of 496 oesophageal carcinoma cases in 12.8 million person-years (i.e. squamous-cell carcinoma, 269, 54.23%), adenocarcinoma,147, 29.64% and unspecified cases, 80,16.13%) were diagnosed. The overall age-standardized incidence rate (per 105 person-years) of oesophageal carcinoma during the study period was 10.51 (95% CI: 6.62-14.41). The APC analysis results showed that the age and birth cohort effects were the significant determinants of declining, and subsequently steadying the oesophageal carcinoma incidence rates. CONCLUSIONS: A substantial decline in oesophageal carcinoma incidence rates was recorded, which significantly varied in all three temporal dimensions. The observed birth cohort patterns suggest changing lifestyle and dietary patterns seem to be responsible for decreasing oesophageal carcinoma risk in Kuwait. Future studies may look for the component causes maintaining the endemicity of oesophageal carcinoma risk in this and similar countries in the region.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".