Five-Year Incidence of Amyotrophic Lateral Sclerosis in British Columbia (2010-2015)
Bibliographic record
Abstract
BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a fatal degenerative neurological disease with significant effects on quality of life. International studies continue to provide consistent incidence values, though complete case ascertainment remains a challenge. The Canadian population has been understudied, and there are currently no quantitative data on the incidence of ALS in British Columbia (BC). The objectives of this study were to determine the five-year incidence rates of ALS in BC and to characterize the demographic patterns of the disease. METHODS: The capture-recapture method was employed to estimate ALS incidence over a five-year period (2010-2015). Two sources were used to identify ALS cases: one database from an ALS medical centre and another from a not-for-profit ALS organization. RESULTS: During this time period, there were 690 incident cases within the two sources. The capture-recapture method estimated 57 unobserved cases, corresponding to a crude five-year incidence rate of 3.29 cases per 100,000 (CI 95%=3.05-3.53). The mean age of diagnosis was 64.6 (CI 95%=59.7-69.4), with 63.5 (CI 95%=56.9-70.1) for men and 65.7 (CI 95%=58.6-72.7) for women. There was a slight male preponderance in incidence, with a 1.05:1 ratio to females. Peak numbers in incidence occurred between the ages of 70 and 79. CONCLUSIONS: The incidence of ALS in BC was found to be consistent with international findings though nominally higher than that in other Canadian provinces to date.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".