Tau in Atypical Parkinsonisms: A Meta‐Analysis of in Vivo <scp>PET</scp> Imaging Findings
Bibliographic record
Abstract
Abstract Background Progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD) are atypical parkinsonisms (APs) that are classified as tauopathies. Patients with these APs may present with similar early clinical manifestations to Parkinson's disease (PD), but they prove unresponsive to anti‐parkinsonian medications. Objective The main objective of this meta‐analysis was to compare first‐ and second‐generation tau PET tracer efficacy in patients with the APs to identify potential diagnostic biomarkers. Methods PubMed and Web of Science were searched between January 1, 1999 and December 31, 2022. We included case–control studies that were published in English and report tau PET tracer binding as mean ± SD in at least one region of interest (ROI). Differences in tau PET binding values were meta‐analyzed using random‐effects meta‐analytic models and subgroup analyses based on ROIs in the statistical programming language R (version 4.2.1). Results Overall, 29 studies with 665 patients were included in the final review. [18F]PI‐2620 outperformed first‐generation tracers when comparing PSP‐HC (g = −1.68, 95% CI: −2.05 to −1.30) and CBD‐HC (g = −1.37, 95% CI: −2.25 to −0.49). When comparing PSP‐PD, the first‐generation tracer, [18F]AV‐1451, presented with higher binding to PSP patients (g = −0.80, 95% CI: −1.24 to −0.35). Conclusions Our results demonstrate the efficacy of [18F]PI‐2620 PET in imaging AP‐tau. These findings contribute towards identifying a diagnostic imaging biomarker for patients with APs. The main limitation of this study was the heterogeneity of the results. Future studies should conduct AP‐PD comparisons with second‐generation tracers to confirm the preliminary results found here.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.039 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".