Laboratory‐Supported Multiple System Atrophy beyond Autonomic Function Testing and Imaging: A Systematic Review by the <scp>MoDiMSA Study Group</scp>
Bibliographic record
Abstract
BACKGROUND: Neuroimaging has been used to support a diagnosis of possible multiple system atrophy (MSA). Only blood pressure changes upon standing are included in the second consensus criteria but other autonomic function tests (AFT) are also useful to diagnose widespread and progressive autonomic failure typical of MSA. Additional diagnostic tools are of interest to improve accuracy of MSA diagnosis. OBJECTIVES: To assess the utility of diagnostic tools beyond brain imaging and AFT in enhancing a laboratory-supported diagnosis of MSA to support the upcoming revision of the consensus criteria. METHODS: The International Parkinson and Movement Disorders Society MSA Study Group (MoDiMSA) performed a systematic review of original papers on biomarkers, sleep studies, genetic, neuroendocrine, neurophysiological, neuropsychological and other tests including olfactory testing and acute levodopa challenge test published before August 2019. RESULTS: Evaluation of history of levodopa responsiveness and olfaction is useful in patients in whom MSA-parkinsonian subtype is suspected. Neuropsychological testing is useful to exclude dementia at time of diagnosis. Applicability of sphincter EMG is limited. When MSA-cerebellar subtype is suspected, a screening for the common causes of adult-onset progressive ataxia is useful, including spinocerebellar ataxias in selected patients. Diagnosing stridor and REM sleep behavior disorder is useful in both MSA subtypes. However, none of these tools are validated in large longitudinal cohorts of postmortem confirmed MSA cases. CONCLUSIONS: Despite limited evidence, additional laboratory work-up of patients with possible MSA beyond imaging and AFT should be considered to optimize the clinical diagnostic accuracy.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".