Structural and metabolic changes in the brain of patients with upper motor neuron disorders: A multiparametric MRI study
Bibliographic record
Abstract
Our objective was to assess and compare the diagnostic sensitivity of conventional MRI (cMRI), magnetization transfer imaging (MTI), diffusion-weighted imaging (DWI), and proton magnetic resonance spectroscopic imaging ((1)H-MRSI) in patients with amyotrophic lateral sclerosis (ALS) and primary lateral sclerosis (PLS). Thirty-eight ALS patients, nine PLS patients, and 22 healthy controls were enrolled. cMRI, MTI, DWI and (1)H-MRSI were obtained. ALS patients were classified as advanced phase (Ap)-ALS (definite+probable) and early phase (Ep)-ALS (possible+probable-laboratory supported). cMRI was highly sensitive in detecting corticospinal tract (CST) hyperintensities in Ap-ALS (63.4%) and PLS (71.9%), but it was poorly sensitive in Ep-ALS (17.1%). Hyperintensity on proton density-weighted images correlated with ALS severity (p=0.02). CST apparent diffusion coefficient was significantly increased in ALS (p<0.01) and PLS (p=0.02) versus controls. The N-acetylaspartate/creatine ratio was significantly reduced in the motor cortex of patients versus controls (p< or = 0.01 in PLS, p=0.02 in Ap-ALS). The study shows the utility of cMRI for diagnosing ALS. Nevertheless, MRI sensitivity is limited at the early stages of the disease. In these cases, DWI and (1)H-MRSI seem to have the potential to ameliorate the patients' work-up and estimate the nature and extent of the underlying pathological damage.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".