Cognitive Composites Domain Scores Related to Neuroimaging Biomarkers within Probable-Amnestic Mild Cognitive Impairment-Storage Subtype
Bibliographic record
Abstract
The probable-amnestic (Pr-a) mild cognitive impairment (MCI)-storage subtype is a phenotype with 8.5 times more risk of conversion to dementia, mainly Alzheimer's disease (AD), than the possible non-amnestic (Pss-na) MCI. The aim of this study was to find the optimized cognitive composites (CCs) domain scores most related to neuroimaging biomarkers within Pr-aMCI-storage subtype patients. The Fundació ACE (ACE) study with 20 Pr-aMCI-storage subtype subjects (MCI) were analyzed. All subjects underwent a neuropsychological assessment, a structural MRI, FDG-PET, and PIB-PET. The adjusted hippocampal volume (aHV) on MRI, the standard uptake value ratio (SUVR) on FDG-PET and PIB-PET SUVR measures were analyzed. The construction of the CCs domain scores, and the aHV on MRI and FDG-PET SUVR measures, were replicated in the parental AB255 study database (n = 133 MCI). Partial correlations adjusted by age, gender, and education were calculated with the associated p-value among every CC domain score and the neuroimaging biomarkers. The results were replicated in the "MCI due to AD" with memory storage impairments from ADNI. Delayed Recall CC domain score was significantly correlated with PIB-PET SUVR (β= -0.61, p = 0.003) in the ACE study and also with aHV on MRI (β= 0.27, p = 0.01) and FDG-PET SUVR (β= 0.27, p = 0.01) in the AB255 study. After a median survival time of 20.6 months, 85% from the ACE MCI converted to AD. The replication of our results in the ADNI dataset also confirmed our findings. Delayed Recall is the CC domain score best correlated with neuroimaging biomarkers associated with prodromal AD diagnosis.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".