NEUROSURGICAL TREATMENT OF TREMOR IN ANTI-MYELIN-ASSOCIATED GLYCOPROTEIN NEUROPATHY
Bibliographic record
Abstract
Anti-myelin-associated glycoprotein (anti-MAG) neuropathy is an antibody-mediated demyelinating neuropathy, with monoclonal immunoglobulin M (IgM) antibodies. It is characterized by a progressive, symmetric, mainly sensory neuropathy and mild distal weakness.1,2 Gait disorder with ataxia is a prominent feature.2 Limb tremor is common, while head, chin, and voice tremor do not occur.2 We report a case of a patient with anti-MAG neuropathy and tremor who had a marked improvement in tremor and quality of life with unilateral thalamic deep brain stimulation (DBS). There have been few other case reports in the literature regarding neuropathic tremor and DBS.3 ### Case report. A right-handed 82-year-old woman with no family history of tremor presented with hand and feet paresthesia, followed by the development of hand tremor 12 months later. Diagnosis of anti-MAG neuropathy was made serologically. Neurophysiologic studies confirmed the presence of a demyelinating, sensorimotor neuropathy with prolongation of the distal motor latencies. The tremor was disabling and prevented feeding and dressing herself as well as performing personal hygiene activities. Corticosteroids, plasma exchange, and IV cyclophosphamide were tried for 12 months without benefit. Primidone and gabapentin also gave little effect. Propranolol was contraindicated due to asthma. Rituximab …
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".