The Canadian ALS Neuroimaging Consortium (CALSNIC) - a multicentre platform for standardized imaging and clinical studies in ALS
Bibliographic record
Abstract
Abstract Background Amyotrophic lateral sclerosis (ALS) is a disabling and rapidly progressive neurodegenerative disorder. Increasing age is an important risk factor for developing ALS, thus the societal impact of this devastating disease will become more profound as the population ages. A significant hurdle to finding effective treatment has been an inability to accurately quantify cerebral degeneration associated with ALS in humans. Advanced magnetic resonance imaging (MRI) techniques hold promise in providing a set of biomarkers to assist in aiding diagnosis and in efficiently evaluating new drugs to treat ALS. Methods The Canadian ALS Neuroimaging Consortium (CALSNIC) was founded to develop and evaluate advanced MRI-based biomarkers that delineate biological heterogeneity, track disease progression, and predict survival in a large and heterogeneous sample of ALS patients. Findings CALSNIC has launched two studies to date (CALSINC-1, CALSNIC-2), acquiring multimodal neuroimaging, neurological, neuropsychological data, and neuropathological data from ALS patients and healthy controls in a prospective and longitudinal fashion from multiple centres in Canada and, more recently, the United States. Clinical and MRI protocols are harmonized across research centres and different MR vendors. Interpretation CALSNIC provides a multicentre platform for studying ALS biology and developing MRI-based biomarkers. Funding Canadian Institutes of Health Research, ALS Society of Canada, Brain Canada Foundation, Shelly Mrkonjic Research Fund
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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 | 0.073 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".