Clinical Spectrum of Stiff Person Syndrome: A Review of Recent Reports
Bibliographic record
Abstract
BACKGROUND: "Classic" stiff person syndrome (SPS) features stiffness, anti-glutamic acid decarboxylase (anti-GAD) antibodies, and other findings. Anti-GAD antibodies are also detected in some neurological syndromes (such as ataxia) in which stiffness is inconsistently present. Patients with otherwise "classic" SPS may either lack anti-GAD antibodies or be seropositive for others. Hence, SPS cases appear to fall within a clinical spectrum that includes conditions such as progressive encephalomyelitis with rigidity and myoclonus (PERM), which exhibits brainstem and autonomic features. We have compiled herein SPS-spectrum cases reported since 2010, and have segregated them on the basis of likely disease mechanism (autoimmune, paraneoplastic, or cryptogenic) for analysis. METHODS: The phrases "stiff person syndrome", "PERM", "anti-GAD antibody syndrome", and "glycine receptor antibody neurological disorders" were searched for in PubMed in January 2015. The results were narrowed to 72 citations after excluding non-English and duplicate reports. Clinical descriptions, laboratory data, management, and outcomes were categorized, tabulated, and analyzed. RESULTS: Sixty-nine autoimmune, 19 paraneoplastic, and 13 cryptogenic SPS-spectrum cases were identified. SPS was the predominant diagnosis among the groups. Roughly two-thirds of autoimmune and paraneoplastic cases were female. Anti-GAD antibodies were most frequently identified, followed by anti-amphiphysin among paraneoplastic cases and by anti-glycine receptor antibodies among autoimmune cases. Benzodiazepines were the most commonly used medications. Prognosis seemed best for cryptogenic cases; malignancy worsened that of paraneoplastic cases. DISCUSSION: Grouping SPS-spectrum cases by pathophysiology provided insights into work-up, treatment, and prognosis. Ample phenotypic and serologic variations are present within the categories. Ruling out malignancy and autoimmunity is appropriate for suspected SPS-spectrum cases.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".