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Record W4312086034 · doi:10.1002/alz.069439

Drug therapy, imaging, and other aspects of clinical management change after Alzheimer’s biomarker testing in routine practice: Findings from the IMPACT‐AD BC study

2022· article· en· W4312086034 on OpenAlexaffabout
David Yang, John R. Best, Colleen Chambers, Howard Feldman, Jacqueline A. Pettersen, Alexander Henri‐Bhargava, Philip E. Lee, Haakon B. Nygaard, Clark Funnell, Dean Foti, Ging‐Yuek Robin Hsiung, Mari L. DeMarco

Bibliographic record

VenueAlzheimer s & Dementia · 2022
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversity of Northern British ColumbiaSimon Fraser UniversityUniversity of British Columbia
Fundersnot available
KeywordsMedicineDementiaBiomarkerDiseaseNeuropsychologyReferralInternal medicineOncologyCognitionIntensive care medicinePsychiatryFamily medicine

Abstract

fetched live from OpenAlex

Abstract Background While previous studies have demonstrated the effect of Alzheimer’s disease (AD) CSF testing in changing diagnosis, we lack an understanding of how this testing affects clinical management. Therefore, we assessed changes in clinical management associated with AD CSF biomarker testing when ordered as part of routine clinical management. Method The ‘Investigating the Impact of Alzheimer’s Disease Diagnostics in British Columbia’ (IMPACT‐AD BC) study (NCT05002699) is a longitudinal study examining the impact of AD CSF testing on clinical management, personal utility and health care economics in British Columbia, Canada. After AD CSF testing was ordered as part of routine care (where the clinical scenario met the appropriate use criteria), the patient and their physician were eligible to participate in the study. The primary outcome was the change in management (pre‐ v. post‐biomarker results) in a composite measure including 1) AD drug therapy, 2) other relevant drug therapy, 3) other diagnostic procedures, and 4) referral or counselling. Result Participants (n = 129) had a median age of 63 (IQR:58‐68); 49% were female. Cognitive impairment at baseline consisted of 7% with subjective cognitive impairment, 53% with mild cognitive impairment, and 40% with dementia. CSF biomarker profiles were consistent with an amyloid‐beta pathology (i.e., A+) in 72% of cases. Changes in clinical management because of testing occurred in 83% of cases including: referrals and counseling (57%), imaging (47%) and other diagnostic procedures (e.g., neuropsychological testing) (42%), and use of AD drug therapies (40%). For those with a non‐AD pre‐biomarkers diagnosis, 42% were changed to AD post‐biomarkers; for those with an AD pre‐biomarkers diagnosis, 18% were changed to non‐AD post‐biomarkers. Conclusion This study has revealed substantial changes in clinical management as a direct result of AD CSF biomarker testing in routine care. An understanding of the implications of biomarker testing will in turn help us: improve appropriate utilization, understand the broader impacts on persons living with dementia and on the health care system, and prepare for expanded use of this testing with the availability of disease‐modifying therapeutics.

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)0.009
metaresearch head score (Gemma)0.039
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 score0.294
Threshold uncertainty score0.584

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.039
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.004
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.002
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 head0.110
GPT teacher head0.415
Teacher spread0.305 · 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

Citations1
Published2022
Admission routes2
Has abstractyes

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