Biomarkers of disease progression in progressive supranuclear palsy for use in clinical trials
Bibliographic record
Abstract
Progressive supranuclear palsy (PSP) is a rare neurodegenerative disease with no current disease-modifying treatments approved. Longitudinal research and clinical trials for PSP are ongoing and require reliable measures that are sensitive to disease progression. Despite susceptibility to subjective limitations, clinical and cognitive assessments are the most used instruments in therapeutic trials in PSP. The objective of this review was to identify measures that have been studied longitudinally as measures of progression and are suitable for use as clinical trial endpoints. We reviewed the measures currently used as trial endpoints, identifying the clinical, cognitive, fluid and imaging measures that have previously been studied longitudinally, and discuss current diagnostic and emerging measures that are yet to be studied longitudinally but that may be sensitive to disease progression. We found that many fluid and imaging measures require further research to validate their use as longitudinal measures of change, including emerging measures that have not yet been studied specifically in PSP. We also summarize the sample size estimates required to detect changes in a two-arm, 52-week therapeutic trial and found that specific MRI volumes require the smallest sample sizes to detect change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".