Current and future applications of brain magnetic resonance imaging in ARSACS
Bibliographic record
Abstract
Magnetic Resonance Imaging (MRI) is a tool with an unquestionable role in the study of neurodegenerative disorders, both for diagnostic purposes and for its ability of providing imaging-derived biomarkers with a growing central role as reliable outcomes in clinical trials. This is even more relevant when dealing with rare disorders such as the Autosomal Recessive Spastic Ataxia of Charlevoix-Saguenay (ARSACS), in which the search of diagnostic and prognostic biomarker is crucial. Due to the rarity of this condition, a comprehensive knowledge of MRI signs observed in ARSACS is lacking. Furthermore, many domains remain still unexplored in ARSACS, especially with reference to the application of advanced imaging techniques that could shed light on the pathophysiological mechanisms of brain damage in this disorder. In this review, after a brief introduction on the major conventional and advanced MRI techniques that can used for diagnostic and research purposes, we present current neuroradiological knowledge in ARSACS. Having discussed strength and weak points of conventional and advanced imaging findings, we also suggest possible future research in this neurologically complex clinical condition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".