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Record W7116953454 · doi:10.1002/alz70861_108606

Real‐World Insights on the Lecanemab Patient Pathway in Early Alzheimer’s Disease in the United States

2025· article· en· W7116953454 on OpenAlexaboutno aff
Michael Rosenbloom, Marwan N. Sabbagh, Jose Soria‐Lopez, Gregory Cooper, Samuel Giles, Cara Leahy, Martin Sadowski, Curtis P. Schreiber, Paul E. Schulz, David C. Weisman, Christian Camargo, Brooke Allen, Courtney Adams, Daryl Jones

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsDiseaseInterimSet (abstract data type)Clinical PracticeMEDLINEPatient care

Abstract

fetched live from OpenAlex

BACKGROUND: Lecanemab-irmb (LEQEMBI®) is indicated for the treatment of patients with Alzheimer's disease (AD) in the mild cognitive impairment or mild dementia stage. The optimized patient pathway enables early and accurate diagnosis of AD and supports patients and care partners through initiation of treatment and appropriate monitoring. This analysis described the lecanemab patient pathway across different medical centers and/or healthcare facilities in the United States. METHOD: This multicenter, retrospective case series and patient pathway study was conducted in 15 geographically diverse neurology clinics, each abstracting deidentified medical chart data for up to 25 patients receiving lecanemab (≥7 infusions) and 1 neurologist per site completing an electronic survey plus an interview. Data collected included sociodemographic characteristics, clinical characteristics, AD diagnosis, and lecanemab use, in addition to practice characteristics and norms to assess best practices. This interim analysis (cutoff date: April 11, 2025) includes ∼25% of the expected total study population (final data cut: May 23, 2025). The protocol received central institutional review board exemption. RESULT: This is an interim analysis of 7 surveys out of a possible 15 surveys completed by neurologists. Practice characteristics for the centers surveyed are illustrated in the Table. On average, 168 patients are treated with lecanemab at each center. They are mostly diagnosed using CSF or amyloid PET, and APOE testing is performed either at the initial consultation (n =3) or after amyloid pathology is confirmed (n =3). Most patients treated with lecanemab are over 65 years old (81%), access their infusions within an outpatient/clinical facility, and receive their first infusion within 4 months of diagnosis. Common cognitive assessments used include Mini-Mental State Examination (MMSE; n=5) and the Montreal Cognitive Assessment (MoCA; n=6). Clinical assessments are generally conducted in person every 6 months. CONCLUSION: This interim analysis indicates a relatively consistent application of diagnostic and treatment protocols across the lecanemab patient pathway. In the full data set analysis (data cutoff: May 23, 2025), practice characteristics will be further reviewed through interviews to identify needs and opportunities that could guide initiatives aimed at supporting the real-world integration of lecanemab into standard clinical care.

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 score0.050
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
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 head0.030
GPT teacher head0.304
Teacher spread0.274 · 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
Published2025
Admission routes1
Has abstractyes

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