P.102 Clinical predictors of disease progression and survival in ALS: insights from the Canadian Neuromuscular Disease Registry
Bibliographic record
Abstract
Background: Amyotrophic Lateral Sclerosis (ALS) leads to progressive functional decline and reduced survival. Identifying clinical predictors like ALSFRS-R and FVC is essential for prognosis and disease management. Understanding progression profiles based on diagnostic characteristics supports clinical trial design and assessment of treatment response. This study evaluates disease progression and survival predictors in ALS patients from the CNDR. Methods: 1565 ALS patients in the CNDR were analyzed to assess baseline ALSFRS-R, FVC, time from symptom onset to diagnosis, and their association with disease progression and survival. Results: At diagnosis, ALSFRS-R was 44.7 (SD = 5.46), with 72.3% scoring ≥44. Mean FVC was 84.2% (SD = 23.3), with 78.3% of patients having FVC ≥65%. ALSFRS-R declined at 1.06 points/month (SD = 1.33), with faster progression in patients diagnosed within 24 months (1.61 points/month). Patients with ALSFRS-R ≥44 had a median survival of 41.8 months, compared to 30.9 months for those <44 (p < 0.001). Similarly, FVC ≥65% was associated with longer survival (35.4 vs. 29.5 months, p = 0.002). Conclusions: ALSFRS-R and FVC at diagnosis predict survival and inform clinical decision-making. These findings highlight the importance of early diagnosis and targeted interventions to slow disease progression and improve patient outcomes.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".