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Record W2418466681 · doi:10.1097/wco.0000000000000210

Imaging pathological tau in atypical parkinsonian disorders

2015· review· en· W2418466681 on OpenAlexafffund
Sarah Coakeley, Antonio P. Strafella

Bibliographic record

VenueCurrent Opinion in Neurology · 2015
Typereview
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsUniversity Health NetworkUniversity of TorontoCentre for Addiction and Mental HealthToronto Western Hospital
FundersCanadian Institutes of Health Research
KeywordsProgressive supranuclear palsyCorticobasal degenerationTau pathologyMedicineParkinson's diseasePathologicalNeurosciencePet imagingBiomarkerDiseasePathologyPsychologyAlzheimer's diseasePositron emission tomographyChemistry

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: This review examines the current literature on tau imaging in atypical parkinsonian disorders and other tauopathies. RECENT FINDINGS: There are a number of tau PET radiotracers that have demonstrated promising preliminary results in atypical parkinsonian disorders, such as progressive supranuclear palsy and corticobasal degeneration. These radiotracers were capable of selectively labeling tau in vitro and in vivo, with high affinity. Other radiotracers tested more extensively in patients with Alzheimer's disease have also been able to successfully image tau deposition. SUMMARY: The development of tau radioligands for PET has led to the current testing of these tracers in clinical studies, many of which concentrate on patients with Alzheimer's disease. Atypical parkinsonian disorders such as progressive supranuclear palsy and corticobasal degeneration are now being investigated as well. These disorders can be very difficult to diagnose, because of their clinical overlap with other parkinsonian disorders. Imaging tau using PET could serve as a diagnostic biomarker for these tauopathies and provide a means of assessing treatment that targets tau burden.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.149
GPT teacher head0.451
Teacher spread0.303 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations5
Published2015
Admission routes2
Has abstractyes

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