Similar birth-cohort patterns in Crohn’s disease and multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: The etiology of Crohn's disease and multiple sclerosis is unknown. Genetic susceptibility and environmental factors are believed to play a role in both diseases. OBJECTIVES: To compare the long-term time trends of the two diseases and thus gain insight about their etiology. METHODS: We analyzed mortality data of Crohn's disease and multiple sclerosis from Canada, England, Italy, the Netherlands, Switzerland, and the United States during the past 60 years. Age-period-cohort (APC) analyses based on logit models served to disentangle the separate influences of age, period, and cohort effects on the overall time trends. RESULTS: The long-term time trends of Crohn's disease and multiple sclerosis have been shaped by strikingly similar birth-cohort patterns. In both diseases alike, mortality increased in all generations born prior to 1910. It peaked among generations born between 1910 and 1930 and then declined in all subsequent generations. Similar birth-cohort patterns of Crohn's disease and multiple sclerosis were found in each country analyzed separately. CONCLUSION: The birth-cohort patterns indicate that the development of Crohn's disease and multiple sclerosis is influenced by exposure to environmental risk factors during an early period of life. These environmental risk factors may be similar or even identical in Crohn's disease and multiple sclerosis.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".