P.027 Incidence of amyotrophic lateral sclerosis in Newfoundland and Labrador
Bibliographic record
Abstract
Background: There is a paucity of research regarding ALS epidemiology in Canada. Previously published data from Newfoundland and Labrador (NL) demonstrate an average incidence of 2.4/100,000 from 2000-2004 (peak 3.3 in 2001, the highest reported in Canada). Local neurologists believe that the incidence has continued to increase. Methods: Clinicians affiliated with the electromyography (EMG) lab at the Health Sciences Centre in St. John’s compiled a list of patients diagnosed with ALS from 2012-2016, based on recall. Their medical records were reviewed and demographic information collected. This was cross-referenced with new referrals to the ALS Society NL per year. Results: Based on new referrals to ALS Society NL the average incidence between 2012-2016 was 2.81/100,000 (peak 3.6 in 2015). Average age-adjusted incidence from the EMG lab was 1.33 (peak 1.73 in 2016). The EMG lab documented a crude incidence of 3.97 in 2018. Conclusions: The incidence of ALS in NL is increased compared to the usual incidence of 1-2/100,000 per year. After the preliminary study, the EMG lab maintained more thorough records and an incidence of 3.97/100,000 was found in 2018. This makes a compelling argument for future research which could explore potential genetic or environmental causes for the increased incidence in this population.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".