Using Neuropsychological Process Scores to Identify Subtle Cognitive Decline and Predict Progression to Mild Cognitive Impairment
Bibliographic record
Abstract
BACKGROUND: We previously operationally-defined subtle cognitive decline (SCD) in preclinical Alzheimer's disease (AD) using total scores on neuropsychological (NP) tests. NP process scores (i.e., provide information about how a total NP score was achieved) may be a useful tool for identifying early cognitive inefficiencies prior to objective impairment seen in mild cognitive impairment (MCI) and dementia. OBJECTIVE: We aimed to integrate process scores into the SCD definition to identify stages of SCD and improve early detection of those at risk for decline. METHODS: Cognitively "normal" participants from the Alzheimer's Disease Neuroimaging Initiative were classified as "early" SCD (E-SCD; >1 SD below norm-adjusted mean on 2 process scores or on 1 process score plus 1 NP total score), "late" SCD (L-SCD; existing SCD criteria of >1 SD below norm-adjusted mean on 2 NP total scores in different domains), or "no SCD" (NC). Process scores considered in the SCD criteria were word-list intrusion errors, retroactive interference, and learning slope. Cerebrospinal fluid AD biomarkers were used to examine pathologic burden across groups. RESULTS: E-SCD and L-SCD progressed to MCI 2.5-3.4 times faster than the NC group. Survival curves for E-SCD and L-SCD converged at 7-8 years after baseline. The combined (E-SCD+L-SCD) group had improved sensitivity to detect progression to MCI relative to L-SCD only. AD biomarker positivity increased across NC, SCD, and MCI groups. CONCLUSIONS: Process scores can be integrated into the SCD criteria to allow for increased sensitivity and earlier identification of cognitively normal older adults at risk for decline prior to frank impairment on NP total scores.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".