Long‐Duration Progressive Supranuclear Palsy: Clinical Course and Pathological Underpinnings
Bibliographic record
Abstract
OBJECTIVES: To identify the clinical characteristics of the subgroup of benign progressive supranuclear palsy with particularly long disease duration; to define neuropathological determinants underlying variability in disease duration in progressive supranuclear palsy. METHODS: Clinical and pathological features were compared among 186 autopsy-confirmed cases with progressive supranuclear palsy with ≥10 years and shorter survival times. RESULTS: The 45 cases (24.2%) had a disease duration of ≥10 years. The absence of ocular motor abnormalities within the first 3 years from disease onset was the only significant independent clinical predictor of longer survival. Histopathologically, the neurodegeneration parameters in each survival group were paralleled anatomically by the distribution of neuronal cytoplasmic inclusions, whereas the tufted astrocytes displayed anatomically an opposite severity pattern. Most interestingly, we found significantly less coiled bodies in those who survive longer, in contrast to patients with less favorable course. INTERPRETATION: A considerable proportion of patients had a more "benign" disease course with ≥10 years survival. They had a distinct pattern and evolution of core symptoms compared to patients with short survival. The inverted anatomical patterns of astrocytic tau distribution suggest distinct implications of these cell types in trans-cellular propagation. The tempo of disease progression appeared to be determined mostly by oligodendroglial tau, where the high degree of oligodendroglial tau pathology might affect neuronal integrity and function on top of neuronal tau pathology. The relative contribution of glial tau should be further explored in cellular and animal models. ANN NEUROL 2022;92:637-649.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".