Longitudinal analysis of glymphatic function in amyotrophic lateral sclerosis and primary lateral sclerosis
Bibliographic record
Abstract
Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder of motor neurons in the brain and spinal cord. Accumulation of misfolded proteins is central to the pathogenesis of ALS and the glymphatic system is emerging as a potential therapeutic target to reduce proteinopathy. Using diffusion tensor imaging analysis along the perivascular spaces (DTI-ALPS) to assess glymphatic function, we performed a longitudinal analysis of glymphatic function in ALS and compared it to a disorder in the motor neuron disease spectrum, primary lateral sclerosis (PLS). From a cohort of 45 participants from the Calgary site in the CALSNIC study (Canadian ALS Neuroimaging Consortium), including 18 ALS, 5 PLS and 22 control participants, DTI-ALPS was analysed and correlated to clinical features (age, sex, disease presentation, disease severity and progression rate) and white matter hyperintensity burden. This included longitudinal measurements at three time points, 4 months apart. The DTI-ALPS index was reduced in ALS participants compared with PLS and control participants across all three time points. There was no association with clinical factors; however, the index tended to decline with advancing age. Our study suggests heterogeneity in glymphatic dysfunction in motor neuron diseases that may be related to the underlying pathogenesis.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".