Pharmacological management of tauopathies
Bibliographic record
Abstract
Tauopathies, a group of neurodegenerative disorders characterized by tau protein aggregation, encompass diseases such as progressive supranuclear palsy, and corticobasal degeneration. While the development of disease-modifying therapies remains a primary research focus, current pharmacological interventions are predominantly directed towards symptomatic management. This review aims to summarize the evidence-based approaches for addressing the motor, autonomic, and systemic manifestations of tauopathies, highlighting both their therapeutic benefits and limitations. For motor dysfunction, dopaminergic agents and muscle relaxants are explored, though their benefits in PSP and CBS are often modest. Sleep disturbances and autonomic dysfunction, which are prevalent but underrecognized in tauopathies, can be managed using targeted pharmacological strategies, with emerging evidence supporting the use of melatonin and other agents. We will discuss the current significant gaps in optimizing these treatments to meet the complex needs of patients and their family. The potential for repurposing existing drugs and the role of novel symptomatic therapies is currently under investigation. By providing an updated synthesis of pharmacological management strategies, clinicians attending this session will get the evidence-based, patient-centered care skills for treatment of PSP and CBS.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".