[P2–225]: UTILITY OF EVENT‐RELATED POTENTIALS IN A MEMORY DISORDERS CLINIC
Bibliographic record
Abstract
Early and accurate diagnosis of Alzheimer's disease (AD) remains central to studying the pathophysiology of AD and to clinical trials aimed at altering disease course. Event-related potentials (ERPs), a type of quantitative electroencephalogram (EEG), are a potential biomarker of AD. Altered ERP signals have been demonstrated in the progression and subsequent conversion to dementia in mild cognitive impairment (MCI) (Papaliagkas, 2011), and have also been detected in presymptomatic individuals (Quiroz et al., 2011). Although ERPs have the potential to be sensitive biomarkers with low cost and low invasiveness, the promise of this technique in clinical practice has not yet been fully realized. Thirty-eight subjects who presented with memory loss underwent standard clinical workup including history and physical, neuroimaging, laboratory studies, and a neuropsychological battery, leading to a clinical diagnosis. All subjects consented to an ERP session using a three-tone auditory oddball paradigm with a seven-electrode device. ERP results were reviewed by two behavioral neurologists blinded to the clinical details of each subject. Amplitude and latency of ERP peaks were rated in the AD or healthy older adult range (Cecchi et al 2015). The total sum of the number of peaks in the diseased range was computed and a diagnostic rating was made for each ERP study as consistent with MCI due to AD, mild AD, moderate AD, or inconsistent with AD. Spearman's correlation showed that the total number of ERP peaks that fell into the mild AD range correlated inversely with Montreal Cognitive Assessment (MOCA); correlation coefficient of -.535 and p-value of .001. (Figure 1).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".