Incidence and Prevalence of Amyotrophic Lateral Sclerosis in Canada: A Systematic Review of the Literature
Bibliographic record
Abstract
BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a fatal progressive neurodegenerative disease of unknown etiology. Although known to be rare, precise information on the frequency of ALS is essential to anticipate future demands on health resources and as baseline information for epidemiological studies. As part of a new ALS epidemiological initiative in Canada, we conducted a systematic review of published incidence and prevalence research in Canada. METHODS: Electronic searches and bibliographic reviews of pertinent publications were conducted. RESULTS: We identified 6 published studies from 4 Canadian provinces conducted between 1974 and 2004; 2 were available only as abstracts. Reported annual incidence rates were similar and study quality was generally good, but there was insufficient detail to adequately assess the methodological quality of 3 of the studies. The most recent studies reported an annual ALS age-adjusted incidence of 2.13 per 100,000 in Nova Scotia (2003-2004) and a crude mean annual incidence of 2.4 per 100,000 in Newfoundland and Labrador (2000-2004). CONCLUSIONS: There are limited data on the frequency of ALS in Canada. We found no studies from 6 of the Canadian provinces or from the territories. Future research is needed to estimate the frequency of occurrence of ALS in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.019 | 0.036 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".