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Record W2811510751 · doi:10.2214/ajr.18.19822

PET/CT of Dementia

2018· review· en· W2811510751 on OpenAlexaff
Katherine Zukotynski, Phillip H. Kuo, David J. Mikulis, Pedro Rosa‐Neto, Antonio P. Strafella, Rathan M. Subramaniam, Sandra E. Black

Bibliographic record

VenueAmerican Journal of Roentgenology · 2018
Typereview
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsSunnybrook Health Science CentreDouglas Mental Health University InstituteUniversity of TorontoUniversity Health NetworkMontreal Neurological Institute and HospitalToronto Western HospitalMcMaster University
Fundersnot available
KeywordsMedicineDementiaNuclear medicineInternal medicineDisease

Abstract

fetched live from OpenAlex

OBJECTIVE: In this article, we review the literature on PET/CT in the management of dementia, present evidence for best clinical practices, and discuss recent advances in the field. CONCLUSION: Standard-of-care imaging for dementia includes CT and MRI, primarily for excluding vascular lesions or masses, detecting atrophy, and monitoring disease severity. PET/CT is a powerful functional modality that can differentiate dementia types and influence management. Fluorine-18-FDG PET/CT reveals the spatial pattern of glucose metabolism in the brain. More recently, radiotracers for PET have been developed that bind to amyloid protein, tau protein, and neuroinflammatory markers.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.974
Threshold uncertainty score0.554

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.042
GPT teacher head0.394
Teacher spread0.353 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations28
Published2018
Admission routes1
Has abstractyes

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