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Record W4206181478 · doi:10.1002/alz.050173

Novel use of tau imaging to enrich for pathological homogeneity within the PERISCOPE‐ALZ study of zagotenemab

2021· article· en· W4206181478 on OpenAlexaboutno aff
Leanne Munsie, Albert Lo, Adam Fleisher, Scott W. Andersen, Sergey Shcherbinin, John R. Sims, Mark A. Mintun

Bibliographic record

VenueAlzheimer s & Dementia · 2021
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsPathologicalMedicineAlzheimer's diseaseCognitive impairmentDiseaseInternal medicinePsychology

Abstract

fetched live from OpenAlex

Abstract Background Zagotenemab (LY3303560), a humanized monoclonal antibody targeting extracellular aggregated tau, is currently in development as a potential disease modifying treatment for early symptomatic Alzheimer’s disease (AD). The PERISCOPE‐ALZ study (Phase 2, NCT03518073) of zagotenemab implements tau PET for classifying AD pathological stage based on the NIA‐AA guidelines (Jack CR, Jr., Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, et al. NIA‐AA Research Framework: Toward a biological definition of Alzheimer's disease. Alzheimers Dement. 2018;14(4):535‐62) and serves as a key eligibility criterion. Here we summarize the study’s screening and baseline characteristics. Method Screening procedures were performed at 60 sites in the United States, Canada, and Japan. Key eligibility criteria include age of 60‐85 years, Mini‐Mental State Exam (MMSE) score of 20‐28, and pre‐defined baseline tau load as assessed by flortaucipir F18 PET. This eligibility strategy was intended to exclude participants with minimal tau burden whose pathology may not be consistent with AD in absence of amyloid pathology confirmation and who are expected to have slower clinical progression. Participants with high tau levels, hypothesized to be less likely to respond to therapy, were also excluded. Result The most common reasons for screen failure were out of range baseline tau levels and cognitive screener score. Of screened participants who underwent a flortaucipir scan, 58% did not meet scan eligibility criteria. Seventy‐nine percent of the flortaucipir scans not meeting eligibility criteria were below pre‐defined tau levels required for inclusion. A higher proportion of females than males who were ineligible based on the tau scan harbored too high of tau levels for eligibility (27% v. 14%, chi‐square p=0.0004). The enrolled cohort had an average age at baseline of 75 years and 53% were female. Average MMSE score at baseline of the enrolled cohort was 23.7. Conclusion PERISCOPE‐ALZ successfully implemented flortaucipir PET imaging as the key inclusion criterion to enroll a pathological homogeneous cohort based on level of tau accumulation. As tau‐targeted therapies for AD advance into clinical research, staged tau pathology as eligibility criterion may be useful in phase 2 proof‐of‐concept trials to test disease modifying therapies. Topline trial results are anticipated in late 2021.

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)0.006
metaresearch head score (Gemma)0.007
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 score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.080
GPT teacher head0.347
Teacher spread0.267 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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