Staging Disease Severity Using the Alzheimer’s Disease Composite Score (ADCOMS): A Retrospective Data Analysis
Bibliographic record
Abstract
The Alzheimer’s disease (AD) composite score (ADCOMS) has been shown to be a more sensitive measure of cognitive change in early AD (i.e., mild cognitive impairment [MCI] and mild AD) than commonly used measures. This study derived ADCOMS value ranges associated with different disease severity stages across the predementia and AD continuum. Data from patients enrolled in the Alzheimer’s Disease Neuroimaging Initiative were assessed at baseline and month 24. Data were randomly split into derivation and validation samples. Receiver-operating characteristic (ROC) curves of ADCOMS values were generated in the derivation sample to assess the sensitivity and specificity of ADCOMS cutoff values compared with existing disease severity cutoff scores using the Clinical Dementia Rating (CDR) global, CDR Sum of Boxes, Alzheimer’s Disease Assessment Scale-Cognitive Subscale, and Mini-Mental State Examination. Optimal ADCOMS cutoff values for each disease stage were compared between the derivation and the validation samples using a χ 2 test. The diagnostic accuracy of the derived ADCOMS cutoff values was then assessed. The analyses were repeated for the subset with positive amyloid β confirmation (Aβ +). The following ADCOMS value ranges for the total population and Aβ + population were < 0.11 indicative of normal cognition, 0.11 to < 0.31 indicative of MCI, 0.31 to 0.77 indicative of mild AD, and > 0.77 indicative of at least moderate AD. The reliability of these ADCOMS value ranges was supported by diagnostic accuracy tests and tests indicating no significant difference in the ROC curves between the derivation and validation samples. ADCOMS values ranges can be used to assess the severity of cognitive decline. The derived severity threshold score ranges for ADCOMS will enable its use as an endpoint in clinical trials assessing disease progression and clinical outcomes of disease-modifying therapies in persons with MCI or early AD, including patients with Aβ + confirmation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 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".