P1‐440: A novel screening approach for MCI/AD
Bibliographic record
Abstract
A practical method for earlier detection of cognitive decline in the elderly, suitable for wide-scale use, is critically needed. Historically cognitive screening has been utilized, and with training, instruments can be administered by allied health personnel. More recently, advances in neuroimaging techniques are yielding promising results but these resources are not readily available to patients in the general community. Biomarkers of AD in the CSF or periphery have potential but lumbar puncture is an invasive method. However, blood for biomarker studies is a suitable alternative as it is easy and well tolerated procedure. In this study, we examined the use of the Montreal Cognitive Assessment (MoCa) with serum beta-amyloid (Aß) 40 and 42 to determine their combined usefulness in diagnosis of Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD). Using a cross-sectional research design, we recruited consenting community-dwelling subjects who were administered the MoCa [and Mini-Mental State Exam (MMSE) for comparison] by trained researchers. All subjects subsequently underwent diagnostic workup involving neurological and neuropsychological evaluation, and if applicable neuroimaging. Thirty subjects enrolled were deemed cognitively normal and 60 were classified as MCI/early AD based on the diagnostic work-up. All blood samples were analyzed in blinded fashion and in duplicates using commercially available Aß40 and Aß42 ELISA kits. Group comparisons are shown in Table 1. Female gender was the only statistically significant demographic variable. As expected, groups differed on MoCa and MMSE, Aß42, and the ratio of Aß42/Aß40. Serum Aß40 did not differ between groups. Table 2 and Figures 1 and 2 show the data from the ROC analyses using individual tests and biomarkers, and in combination. The use of the MoCa and the ratio of Aß42/Aß40 demonstrated the highest sensitivity (96.2%) and specificity (92.9%). The use of MoCa test (a sensitive cognitive screen) and the ratio of serum Aß42/Aß40 proved to be highly sensitive (and specific) for differentiating cognitively normal elderly controls and individuals who were diagnosed with cognitive impairment. This approach is practical and cost-effective and warrants further study, particularly in a primary care setting where most at-risk elders are likely to present. Comparison of performance of MMSE and MoCA in combination with Aβ42/Aβ40 ratio.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".