Bibliographic record
Abstract
Abstract This chapter focuses on the utility of transcranial magnetic stimulation (TMS) for clinical diagnosis and follow-up. We first introduce methods to measure corticospinal excitability, intracortical inhibitory and facilitatory circuits and cortico-cortical connections. We then discuss the use of TMS in several neurological disorders. Central motor conduction time (CMCT) can be used to detect myelopathy and to localize the lesions, while the triple stimulation technique has higher sensitivity. CMCT can also detect upper motor neuron involvement in amyotrophic lateral sclerosis and multiple sclerosis. The ipsilateral silent period and CMCT are helpful for differentiating atypical parkinsonism from Parkinson’s disease. Distinct patterns of cortical excitability findings can be obtained from different genetic forms of hereditary spinocerebellar ataxia. Reduction of short afferent inhibition (SAI) in Alzheimer disease and reduction of short-interval intracortical inhibition (SICI) in fronto-temporal dementia can be reliable indicators to differentiate these two diseases. Patients with diffuse Lewy body dementia and hallucination also have reduced SAI. The results of motor evoked potential measurements in the early stage of stroke are predictive of the long-term motor outcome. SICI, CMCT, and rest motor threshold are useful measurements to monitor extent of clinical disability. We conclude that TMS has clinical diagnostic utility in a broad range of neurological diseases.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".