Critical appraisal of the role of davunetide in the treatment of progressive supranuclear palsy
Bibliographic record
Abstract
Progressive supranuclear palsy (PSP) is a rare neurodegenerative disease characterized by the accumulation of tau protein aggregates in the basal ganglia, brainstem and cerebral cortex leading to rapid disease progression and death. The neurofibrillary tangles that define the neuropathology of PSP are comprised of aggregated 4R tau and show a well-defined distribution. Classically, PSP is diagnosed by symptoms that include progressive gait disturbance, early falls, vertical ophthalmoparesis, akinetic-rigid features, prominent bulbar dysfunction and fronto-subcortical dementia. There are currently no effective therapies for the treatment of this rapidly degenerating and debilitating disease. Davunetide is a novel neuroprotective peptide that is thought to impact neuronal integrity and cell survival through the stabilization of microtubules. Preclinical activity in models of tauopathy has been translated to clinical studies, demonstrating pharmacologic activity that has supported further development. Davunetide's efficacy and tolerability are being tested in a placebo-controlled study in PSP patients, making it the most advanced drug candidate in this indication. This review examines the disease characteristics of PSP, the rationale for treating PSP with davunetide and assesses some of the challenges of clinical trials in this patient population.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".