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Record W4403318148 · doi:10.1002/gps.6157

Projected Annual Lecanemab Treatment Eligibility in an Irish Regional Specialist Memory Clinic

2024· article· en· W4403318148 on OpenAlexfundno aff
Eimear Connolly, Antoinette O’Connor, Helena Dolphin, Adam H. Dyer, Aoife Fallon, Seán O’Dowd, Seán Kennelly

Bibliographic record

VenueInternational Journal of Geriatric Psychiatry · 2024
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersHealth Service ExecutiveWellcome TrustMeath FoundationIrish Research eLibraryCanadian Institute for Theoretical Astrophysics
KeywordsMedicineCohortMemory clinicDiseaseClinical trialPhysical therapyCognitive impairmentInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: The advent of Disease Modifying Therapies (DMTs) for the treatment of Alzheimer's Disease (AD) has the potential to transform the lives of those with early AD. Timely identification of eligible patients is needed to ensure treatments are delivered during a narrow window of therapeutic opportunity. Appropriate clinical service design will hinge on improved understanding of future demands, thus there is a pressing need to investigate patient eligibility in real world clinical cohorts. The primary aim of this study is to assess the eligibility by appropriate use criteria (AUC) for lecanemab therapy in a real-world, undifferentiated clinical patient cohort attending a Regional Specialist Memory Clinic (RSMC), with the secondary aims of determining the proportion of patients with biomarker positive Alzheimer's Disease (AD) who would be eligible for lecanemab therapy by AUC. Clinical trial eligibility criteria were also applied to both groups and discrepancies that exist between eligibility rates explored. METHODS: A retrospective cohort study of all new patients attending a RSMC from 1st January 2022 to 31st December 2022 was conducted. Data collected included demographic details, outcomes of diagnostic assessments and comorbidities. MRI images, where indicated, were reviewed. Amyloid positivity was defined as either Amyloid and Tau positive (A+T+) or Amyloid positive with a positive P-Tau/Ab42 ratio on cerebrospinal fluid (CSF) testing. Appropriate use criteria (AUC) and clinical trial criteria for lecanemab were applied. Proportion of eligible patients was calculated. RESULTS: Eleven (5.9%) of 188 new patient attenders were eligible (average age 66.7 years [SD 8.9], 63.6% female) by AUC, with 26.2% of patients with biomarker positive Alzheimer's Disease eligible for lecanemab therapy. The most common reason for exclusion was a lack of biomarker confirmation of AD pathology followed by cognitive ineligibility (based on defined cognitive testing cut-offs) at the time of referral and/or initial assessment. Only 40.4% of patients had CSF testing for AD biomarkers while almost 20% of the patients with biomarker positive AD were excluded due to lack of a screening MRI in the previous 12 months. CONCLUSION: In this study, the potential eligibility rate by AUC of the entire patient cohort (5.9%) was limited by the small proportion of patients who had CSF testing for AD biomarkers. So while disease-modification with Lecanemab is a welcome therapeutic advance, although only a small proportion of people currently attending specialist services will be eligible. Successful delivery of DMTs will require significant resource allocation and optimisation of referral pathways to facilitate early identification of potentially eligible patients.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.051
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.032
GPT teacher head0.417
Teacher spread0.386 · 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

Citations7
Published2024
Admission routes1
Has abstractyes

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