Severity of in vivo corticospinal tract degeneration is associated with survival in amyotrophic lateral sclerosis: a longitudinal, multicohort study
Bibliographic record
Abstract
Abstract Background and purpose This study sought to evaluate the relationship of progressive corticospinal tract (CST) degeneration with survival in patients with amyotrophic lateral sclerosis (ALS). Methods Forty‐one ALS patients and 42 healthy controls were prospectively recruited from the Canadian ALS Neuroimaging Consortium. Magnetic resonance imaging scanning and clinical evaluations were performed on participants at three serial visits with 4‐month intervals. Texture analysis was performed on T1‐weighted magnetic resonance imaging scans and the texture feature ‘autocorrelation’ was quantified. Whole‐brain group‐level comparisons were performed between patient subgroups. Linear mixed models were used to evaluate longitudinal progression. Region‐of‐interest and 3D voxel‐wise Cox proportional‐hazards regression models were constructed for survival prediction. For all survival analyses, a second independent cohort was used for model validation. Results Autocorrelation of the bilateral CST was increased at baseline and progressively increased over time at a faster rate in ALS short survivors. Cox proportional‐hazards regression analyses revealed autocorrelation of the CST as a significant predictor of survival at 5 years follow‐up (hazard ratio 1.28, p = 0.005). Similarly, voxel‐wise whole‐brain survival analyses revealed that increased autocorrelation of the CST was associated with shorter survival. ALS patients stratified by median autocorrelation in the CST had significantly different survival times using the Kaplan–Meier curve and log‐rank tests (χ2 = 7.402, p = 0.007). Conclusions Severity of cerebral degeneration is associated with survival in ALS. CST degeneration progresses faster in subgroups of patients with shorter survival. Neuroimaging holds promise as a tool to improve patient management and facilitation of clinical trials.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".