Genetic testing for amyotrophic lateral sclerosis in Canada – an assessment of current practices
Bibliographic record
Abstract
Objective: To understand current genetic testing practices at Canadian ALS clinics. Methods: An online survey and phone interviews, with clinicians practicing in 27 ALS clinics in Canada, were employed to collect data. Quantitative and qualitative analyses were conducted. Results: Ninety-three percent (25/27) of ALS clinics in Canada are routinely ordering genetic testing for familial ALS, while 33% (9/27) of clinics are routinely ordering genetic testing for sporadic ALS. Barriers to genetic testing include a perceived lack of an impact on treatment plan, difficulty in obtaining approvals, primarily from provincial Ministries of Health, and limited access to genetic counseling. Predictive testing practices were found to be the most variable across the country. The average wait time for a symptomatic patient living with ALS to see a genetic counselor in Canada is 10 months (range 0–36 months). Conclusions: Access to genetic testing, and testing practices, vary greatly across Canadian ALS clinics. There may be patients with a monogenetic etiology to their ALS who are not being identified given that genetic testing for patients diagnosed with ALS is not routinely performed at all clinics. This study highlights potential inequities for patients with ALS that can arise from variability in health care delivery across jurisdictions, in a federally-funded, but provincially-regulated, health care system. Clinical trials for both symptomatic ALS patients and pre-symptomatic ALS gene carriers are ongoing, and ALS clinicians in Canada are motivated to improve access to genetic testing for ALS.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".