Diagnostic Challenges in Progressive Supranuclear Palsy: Early Identification and Mimics
Bibliographic record
Abstract
Abstract Background Progressive supranuclear palsy (PSP) is an often‐undiagnosed neurodegenerative disorder. There are often delays in diagnosing PSP. Method Retrospective chart review of all newly diagnosed PSP patients referred to an outpatient clinic. We conducted a review of all initial/new PSP cases in our geriatric clinic. The most common preliminary diagnosis the patients had been given included was idiopathic Parkinson’s disease. Other common diagnoses were vascular Parkinson’s, frontal lobe dementia, psychotic depression and late life psychosis. Clinical records with the subjective accounts, cognitive assessments and brain imaging reports of 23 PSP patients were reviewed. Results The most common cognitive deficits were visuospatial changes (100%), semantic fluency deficits (80.4%), dysexecutive function. Sleep disruption with insomnia with intermittent awakening was also commonly reported. A propensity for falls and multiple falls were also reported. The interval from the onset of symptoms and initial presentation to a clinical provider to diagnosis ranged from 1 ‐ 7 years. Conclusion A high index of suspicion and recognition that PSP might not be as uncommon as previously thought is needed. A timely diagnosis and recognition of the various cognitive and behavioral changes is important.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".