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Record W4312086800 · doi:10.1002/alz.066569

Reducing misdiagnosis of Alzheimer’s Disease pathology utilizing CSF and amyloid PET

2022· article· en· W4312086800 on OpenAlexaboutno aff
Rianne N Esquivel, F. Simone, Natalya Benina, Sara Gannon, Nathalie Le Bastard, Amanda Calabro, Manu Vandijck, Jessica Latham, Diana Dickson, Rachel R. Radwan

Bibliographic record

VenueAlzheimer s & Dementia · 2022
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAmyloid (mycology)PathologyCognitive impairmentDementiaDiseaseMontreal Cognitive AssessmentInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background Utilizing cognitive tests alone, including the Mini‐Mental State Examination (MMSE) or the Montreal Cognitive Assessment (MoCA), cannot detect the presence of amyloid plaques and tangles in Alzheimer’s disease (AD). While in late stages of AD clinical diagnosis relying primarily on cognitive testing is correct 70‐90% of the time, in MCI accuracy is reduced to 50‐60%. Although autopsy is the gold standard for AD diagnosis, amyloid PET imaging and more recently the Lumipulse Gβ‐Amyloid Ratio (Aβ1‐42/Aβ1‐40) are validated to determine amyloid pathology. With the upcoming availability of disease modifying therapies targeting amyloid in MCI and early AD, and anti‐tau drugs in the pipeline, it is necessary to have true measures of AD pathology for a clinical evaluation early in the disease process. Here we examine the performance of cognitive testing alone for identification of amyloid positivity in MCI patients from the ADNI study when compared to amyloid PET and CSF testing. Method A subset of 170 CSF samples from the ADNI biobank (all MCI; ≥ 50 years old; amyloid PET negative (n=74) or amyloid PET positive (n=96)) was used to examine correlation of PET, CSF and MMSE/MoCA scores. Amyloid PET images were derived from an FDA approved tracer (Florbetapir F18) and method. CSF Aβ1‐42 and Aβ1‐40 were measured using the LUMIPULSE G1200 (Fujirebio). Result At a set cutoff of 0.058, Aβ1‐42/Aβ1‐40 demonstrated a PPA and NPA of 85.9% and 93.5% respectively for identifying amyloid PET status in individuals suffering from cognitive complaints. When examining the Aβ1‐42/Aβ1‐40 ratio of the MCI patients compared to amyloid PET in ADNI, Aβ1‐42/Aβ1‐40 achieved an AUC of 0.90. Alternatively, the MMSE score and MOCA score had AUCs with amyloid PET of 0.61 and 0.63 respectively and exhibited poor correlation with CSF. Conclusion CSF measurements or amyloid PET provide greater accuracy in determining amyloid status than cognitive screening tests. MMSE or MoCA as standalone tests give low confidence in a correct pathological diagnosis for MCI patients. Moving forward it will be important to consider PET or CSF results to determine underlying pathology in patients with MCI to ensure proper treatments and avoid unwanted side effects.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.037
GPT teacher head0.316
Teacher spread0.279 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2022
Admission routes1
Has abstractyes

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