EFNS guidelines on the use of neuroimaging in the management of motor neuron diseases
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: These European Federation of Neurological Societies guidelines on neuroimaging of motor neuron diseases (MNDs) are designed to provide practical help for the neurologists to make appropriate use of neuroimaging techniques in patients with MNDs, which ranges from diagnostic and monitoring aspects to the in vivo study of the pathobiology of such conditions. METHODS: Literature searches were performed before expert members of the Task Force wrote proposal. Then, consensus was reached by circulating drafts of the manuscript to the Task Force members and by discussion of the classification of evidence and recommendations. RESULTS AND CONCLUSIONS: The use of conventional MRI in patients suspected of having a MND is yet restricted to exclude other causes of signs and symptoms of MN pathology [class IV, level good clinical practice point (GCPP)]. Although the detection of corticospinal tract hyperintensities on conventional MRI and a T2-hypointense rim in the pre-central gyrus can support a pre-existing suspicion of MND, the specific search of these abnormalities for the purpose of making a firm diagnosis of MND is not recommended (class IV, level GCPP). At present, advanced neuroimaging techniques, including diffusion tensor imaging and proton magnetic resonance spectroscopic imaging, do not have a role in the diagnosis or routine monitoring of MNDs yet (class IV, level GCPP). However, it is strongly advisable to incorporate measures derived from these techniques into new clinical trials as exploratory outcomes to gain additional insights into disease pathophysiology and into the value of these techniques in the (longitudinal) assessment of MNDs (class IV, level GCPP).
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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 | 0.029 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.014 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".