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Record W2060672748 · doi:10.1177/1533317510363468

The Value of PET in Mild Cognitive Impairment, Typical and Atypical/Unclear Dementias: A Retrospective Memory Clinic Study

2010· article· en· W2060672748 on OpenAlexaff
Robert Laforce, James P Buteau, Nancy Paquet, Louis Verret, Michèle Houde, Rémi W. Bouchard

Bibliographic record

VenueAmerican Journal of Alzheimer s Disease & Other Dementias® · 2010
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsCentre Hospitalier Universitaire de SherbrookeHôpital de l'Enfant-Jésus
Fundersnot available
KeywordsDementiaMedicineMedical diagnosisMemory clinicFrontotemporal dementiaPositron emission tomographyRetrospective cohort studyCognitive impairmentMemory impairmentDiseasePet imagingPediatricsInternal medicineCognitionPsychiatryNuclear medicineRadiology

Abstract

fetched live from OpenAlex

This retrospective study examined the role of [(18)F]fluorodeoxyglucose positron emission tomography (FDG-PET) in the diagnosis of atypical/unclear dementias in a memory clinic setting. A total of 94 patients with a diagnosis of mild cognitive impairment (MCI) or dementia, who had a PET study within 2 months of their diagnosis, were reevaluated at 5 and 18 months. Results showed that PET was associated with a change in diagnosis in 29% of patients and a 64% increase in the use of cholinesterase inhibitors (ChEIs). PET significantly lowered the number of atypical/unclear diagnoses from 39.4% to 16% and nearly 30% of these were found to have a typical Alzheimer's disease (AD) pattern of hypometabolism. In conclusion, the addition of PET to the investigation of atypical/unclear cases of dementia helped generating a more accurate diagnosis and initiating earlier treatment. PET was of limited contribution to typical AD and frontotemporal dementia (FTD) cases. This study provides guiding evidence about the true value of PET imaging in the day-to-day challenge of dementia diagnosis.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.966

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.336
Teacher spread0.321 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations50
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of Alzheimer s Disease & Other Dementias®Same topicDementia and Cognitive Impairment ResearchFrench-language works237,207