The clinical and neuroimaging features of sporadic adult-onset neuronal intranuclear inclusion disease
Bibliographic record
Abstract
BACKGROUND: Neuronal intranuclear inclusion disease (NIID) is a rare slowly progressive neurodegenerative disorder that is characterized pathologically by the presence of eosinophilic intranuclear inclusions. NIID is a heterogeneous disease with diverse clinical manifestations, making diagnosis difficult. Here, we analyzed the clinical, pathological, and radiological features of Chinese NIID patients to improve our understanding of NIID. METHODS: A total of 17 patients with sporadic NIID were recruited from the Ruijin Hospital Database between 2014 and 2021. Clinical patient information and brain MRI data were collected. All of the patients underwent standard skin biopsy procedures. RESULTS: The average age of onset for symptoms was 60.18 years, and the average duration of illness was 4.06 years. All patients were diagnosed with NIID due to the presence of intranuclear inclusions confirmed by skin biopsy. Tremor was the most common initial symptom. The average ages at onset and at diagnosis were both lower in patients with tremor than in patients without tremor. NIID may be a systemic disease that affects multiple organs, for one patient had a history of chronic renal insufficiency for more than 10 years. In addition to high-intensity U-fibers signals on diffusion-weighted imaging, there were several other MRI findings, such as focal leukoencephalopathy and cortical swelling. Encephalitic episodes followed by reversible leukoencephalopathy was another important imaging feature of NIID. CONCLUSION: The clinical manifestations of NIID are highly variable. Tremor may be the most common initial symptom in certain cohorts. Encephalitic episodes followed by reversible asymmetric leukoencephalopathy may also indicate this disease.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".