Meta-Analyses of Auditory Evoked Potentials as Alzheimer Biomarkers
Bibliographic record
Abstract
OBJECTIVES: Alterations in auditory evoked potential (AEP) parameters have been associated with sensory memory deficits and may serve as biomarkers for cognitive decline. This systematic review and meta-analysis aimed to evaluate the effectiveness of AEPs in the early detection of Alzheimer disease (AD). DESIGN: The systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines. A comprehensive search was performed across five electronic databases (EMBASE, Scopus, Cochrane Library, Web of Science, PubMed, and CINAHL) from their inception until August 2024, without restrictions on date or language. The methodological quality of evidence was assessed using the Crew Critical Appraisal Tool. Data were extracted on the latency and amplitude of five AEP components, including auditory P50 gating, mismatch negativity, and late-latency responses (N100, N200, P300), comparing patients with AD to age-matched control peers. RESULTS: Out of 437 publications, 54 articles were selected for review, with most rated as having high methodological quality. The analysis revealed a significantly larger P50 gating amplitude ( p < 0.001) in patients with AD. Furthermore, patients with AD demonstrated significantly prolonged latencies and reduced amplitudes for N100, N200, and P300 components ( p ≤ 0.001) compared with controls. Among all AEPs, P300 latency exhibited the largest effect size. Funnel plot analysis and Egger's regression test showed no evidence of publication bias. CONCLUSIONS: Our findings support the clinical utility of AEPs in early AD detection, with the P300 response identified as the most accurate electrophysiological measure for distinguishing patients with AD from the control group. These results highlight the value of incorporating AEPs into clinical assessment protocols to enhance early-stage AD diagnosis and monitoring, thereby facilitating timely interventions and the development of personalized treatment strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.063 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.049 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".