Bibliographic record
Abstract
Abstract The recent development of positron emission tomography radiotracers targeting pathological tau in vivo has led to numerous human trials. While investigations have primarily focused on the most common tauopathy, Alzheimer’s disease, it is imperative that testing also be performed in parkinsonian tauopathies, such as progressive supranuclear palsy, corticobasal degeneration, and frontotemporal dementia and parkinsonism linked to chromosome 17. Tau aggregates differ in isoforms and conformations across disorders, and as a result one radiotracer may not be appropriate for all tauopathies. In this review, we evaluate the preclinical and clinical reports of current tau radiotracers in parkinsonian disorders. These radiotracers include [18F]FDDNP, [11C]PBB3, [18F]THK-5317, [18F]THK-5351, and [18F]AV-1451 ([18F]T807). There are concerns of off-target binding with [18F]FDDNP and [11C]PBB3, which may increase the signal to noise ratio and thereby decrease the efficacy of these radiotracers. Testing in [18F]THK-5317, [18F]THK-5351, and [18F]AV-1451 has been performed in progressive supranuclear palsy, while [18F]THK-5317 and [18F]AV-1451 have also been tested in corticobasal degeneration patients. [18F]THK-5317 and [18F]THK-5351 have demonstrated binding in brain regions known to be afflicted with pathological tau; however, due to small sample sizes these studies should be replicated before concluding their appropriateness in parkinsonian tauopathies. [18F]AV-1451 has demonstrated mixed results in progressive supranuclear palsy patients and post-mortem analysis shows minimal to no binding to non-Alzheimer’s disease tauopathies brain slices.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".