Accuracy of Figure Memory Test of the Brief Cognitive Screening Battery to predict amyloid status in older adults with Subjective Cognitive Decline
Bibliographic record
Abstract
BACKGROUND: Some older adults with subjective decline (SCD) had a positive amyloid biomarker indicating a preclinical stage of Alzheimer's disease. OBJECTIVES: To assess the accuracy of Delayed Recall of Figure Memory Test (DR-FMT) of Brief Cognitive Screening Battery to predict amyloid status in SCD older adults. METHOD: The sample consisted of 45 older adults classified as SCD and 25 as cognitively unimpaired (mean age of 76.4 and 73.5, respectively, p = 0.138). They were evaluated with BCSB and a standard neuropsychological battery (which includes MMSE, MoCA, RAVLT, Logical Memory and DR of Rey Complex Figure). Subjects were underwent PIB-PET to assess their amyloid status and images were classified based on visual and semi-quantitative analyses with 3D-SSP methodology. RESULT: Twelve SCD older adults (27.3%) were positive amyloid against six in the controls (23.1%). In SCD group, DR-FMT was the only memory test that correlated with SUV in amyloid PET (r = -0.514, p < 0.001). Only DR-FMTshowed significant area under the curve (AUC) in the ROC curve in SCD older adults (AUC = 0.771, 95% CI 0.621 - 0.921). Among SCD older adults, DR-FMT < 8.0 had a sensitivity of 83.3%, a specificity of 68.7% and an accuracy of 72.7%. CONCLUSION: FMT proved to have a good sensitivity and accuracy to predict amyloid status in SCD older adults.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".