Assessing the Clinical Meaningfulness of the Alzheimer’s Disease Composite Score (ADCOMS) Tool
Bibliographic record
Abstract
INTRODUCTION: The Alzheimer's Disease Composite Score (ADCOMS) is a tool developed to detect clinical progression and measure treatment effect in patients in early stages of Alzheimer's disease (AD). The psychometric properties of the ADCOMS have been established; however, the threshold for clinical meaningfulness has yet to be identified. METHODS: Anchor-based, distribution-based, and ROC curve analyses were used to estimate clinically meaningful thresholds for change in ADCOMS for patients with mild cognitive impairment (MCI) and AD dementia. This study included data from three sources: the Alzheimer's Disease Neuroimaging Initiative (ADNI), the National Alzheimer's Coordinating Center (NACC), and a legacy dataset that included data from four sources: the placebo group from three MCI trials and an earlier data cut from ADNI. Results were stratified by disease severity (MCI vs. dementia) and APOE ε4 carrier status. RESULTS: A total of 5355 participants were included in the analysis. The ADCOMS was able to detect change for MCI and dementia patients who experienced a meaningful decline in cognition (as defined by the Clinical Dementia Rating Scale Sum of Boxes [CDR-SOB]) between baseline and month 12. The following ADCOMS cut-offs were proposed: 0.05 for MCI and 0.10 for dementia. CONCLUSIONS: The ADCOMS was previously established as a valid and reliable tool for use in clinical trials for MCI due to AD and dementia populations. By defining thresholds for clinically meaningful change of ADCOMS, this work is an important step in interpreting clinical findings and estimates of treatment effects in early stage AD trials.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".