Estimation of the number of people with Down syndrome living in Canada
Bibliographic record
Abstract
PURPOSE: We estimate the live births (LBs), selective terminations, miscarriages, and overall population with Down syndrome (DS) in Canada from 1950 to 2020. This study adds to previous work from the United States, Europe, Australia, and New Zealand. METHODS: The LBs with DS-in the absence of DS-related terminations-were estimated on the maternal age distribution in the general population. Actual LBs were modeled on registry data. We applied constructed survival curves to annual LBs to estimate population numbers. RESULTS: In 2020, there were an estimated 418 LBs with DS in Canada. As a result of DS-related elective terminations, there were 54% fewer children with DS born than potentially could have been born in Canada, as of 2020. The estimated number of people with DS in Canada has increased from 5138 people in 1950 to 22,367 in 2020. CONCLUSION: Although, in recent years, the population size of people with DS is decreasing in Australia, New Zealand, and Europe, the number of people with DS is still growing in the United States and Canada. In Canada, however, the growth rate is increasingly slowing down, probably foreshadowing a population contraction in the coming years.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".