Accuracy of Conventional MRI in ALS
Bibliographic record
Abstract
BACKGROUND: There is currently no definite neuroimaging test to detect amyotrophic lateral sclerosis (ALS), which leads to significant delay in diagnosis, particularly if one takes into account the rapidity of disease evolution. Hyperintensity of the corticospinal tracts (CST) on T2 or fluid-attenuated inversion recovery (FLAIR) weighted magnetic resonance imaging (MRI) has been well described, but data on sensitivity and specificity in larger series is lacking to help guide its application to clinical care. METHODS: We analyzed clinical and MRI data from 64 patients with a definite retrospective diagnosis of ALS. In this case-control study, two experienced blinded neuroradiologists systematically assessed defined rostrocaudal segments of the intracranial course of the CST. RESULTS: The overall sensitivity and specificity of conventional MRI for the diagnosis of ALS were 48% and 76% respectively. Highest specificities for CST hyperintensity were noted for the subcortical white matter (92%), centrum semiovale (88%) and medullary pyramids (92%). The lowest specificities were found for the cerebral peduncle (36%) and internal capsule (32%). We did not find a correlation with the rate of clinical progression, age of onset or the presence of upper motor neuron signs on examination. CONCLUSION: Conventional MRI was not found to be a reliable diagnostic tool for ALS and it did not help predict clinical characteristics such as speed of evolution or prominence of upper motor neuron signs. Its main role in the setting of ALS should remain to help exclude alternative diagnostic considerations. A multimodal approach relying on newer functional and structural MRI techniques still needs to be developed and validated. Précision de l'IRM conventionnelle dans la SLA.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".