Increased number of Purkinje cell dendritic swellings in essential tremor
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Essential Tremor (ET) is among the most prevalent neurologic disorders. Growing clinical and neuro-imaging evidence implicates cerebellar dysfunction in the pathogenesis of ET and emerging postmortem studies have identified structural changes in the cerebellum, particularly in Purkinje cells. In this study we systematically quantified focal Purkinje cell dendritic swellings (DS) in 20 ET vs. 19 control brains. METHODS: In each brain, a standard parasagittal neocerebellar tissue block was harvested. DS were quantified in one 7-μm thick section stained with Luxol Fast Blue/Hematoxylin and Eosin (LH&E) and one section stained with Bielschowsky method. RESULTS: The number of DS were higher in cases than controls by LH&E (1.50 ± 1.79 vs. 0.05 ± 0.23, P = 0.002) and Bielschowsky methods (2.70 ± 3.10 vs. 0.37 ± 0.50, P = 0.002). The number of DS was correlated with the number of torpedoes and marginally inversely correlated with the number of Purkinje cells. CONCLUSION: The current study documents and quantifies an additional structural abnormality in the ET cerebellum, adding to the growing list of such changes in this disease. The mechanisms that underlie this and other structural changes observed in ET are currently unknown, and they deserve additional exploration.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".