A Modified Progressive Supranuclear Palsy Rating Scale for Virtual Assessments
Bibliographic record
Abstract
BACKGROUND: The reliability of the Progressive Supranuclear Palsy Rating Scale (PSPRS) using teleneurology has not been assessed. OBJECTIVES: To test whether removing items inadequately assessed by video would impact measurement of PSP severity and progression. METHODS: We performed secondary analyses of two data sets: the phase 2/3 trial of Davunetide in PSP and a large single-center cohort. We examined two modifications of the PSPRS: (1) removing neck rigidity, limb rigidity, and postural stability (25 items; mPSPRS-25) and (2) also removing three ocular motor items and limb dystonia (21 items; mPSPRS-21). Proportional agreement relative to the possible total scores was measured using the intraclass correlation coefficient, compared to the original PSPRS baseline values and change over 6 and 12 months. We examined the ability of both scales to predict survival in the single-center cohort using proportional hazards models. RESULTS: The mPSPRS-25 showed excellent agreement (0.99; P < 0.001) with the original PSPRS at baseline, 0.98 (P < 0.001) agreement in measuring change over 6 months, and 0.98 (P < 0.001) over 12 months. The mPSPRS-21 showed agreement of 0.94 (P < 0.001) with the original PSPRS at baseline, 0.92 (P < 0.001) at 6 months, and 0.95 (P < 0.001) at 12 months. Baseline and 6-month change in both modified scales were highly predictive of survival in the single-center cohort. CONCLUSIONS: Modified versions of the PSPRS which can be administered remotely show excellent agreement with the original scale and predict survival in PSP. The mPSPRS-21 should facilitate clinical care and research in PSP via teleneurology. © 2022 International Parkinson and Movement Disorder Society.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".