The phenotypic spectrum of progressive supranuclear palsy: A retrospective multicenter study of 100 definite cases
Bibliographic record
Abstract
The phenotypic variability of progressive supranuclear palsy (PSP) may account for its frequent misdiagnosis, in particular in early stages of the disease. However, large multicenter studies to define the frequency and natural history of PSP phenotypes are missing. In a cohort of 100 autopsy-confirmed patients we studied the phenotypic spectrum of PSP by retrospective chart review. Patients were derived from five brain banks with expertise in neurodegenerative disorders with referrals from multiple academic hospitals. The clinical characteristics of the 100 cases showed remarkable heterogeneity. Most strikingly, only 24% of cases presented as Richardson's Syndrome (RS), and more than half of the cases either showed overlapping features of several predescribed phenotypes, or features not fitting proposed classification criteria for PSP phenotypes. Classification of patients according to predominant clinical features in the first 2 years of the disease course allowed a more comprehensive description of the phenotypic spectrum. These predominance types differed significantly with regard to survival time and frequency of cognitive deficits. In summary, the phenotypic spectrum of PSP may be broader and more variable than previously described in single-center studies. Thus, too strict clinical criteria defining distinct phenotypes may not reflect this variability. A more pragmatic clinical approach using predominance types could potentially be more helpful in the early recognition of and for making prognostic predictions for these patients. Given the limitations arising from the retrospective nature of this analysis, a systematic validation in a prospective cohort study is imperative.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".