Evaluation of what matters most in existing clinical outcomes assessments in Alzheimer's disease
Bibliographic record
Abstract
Abstract Background The Alzheimer's Disease Patient and Caregiver Engagement (AD PACE) Initiative works to identify and measure treatment‐related needs, preferences, and priorities of individuals with or at risk for Alzheimer’s disease (AD) and care partners across the continuum of AD. First, the What Matters Most (WMM) study assessed needs, preferences and priorities of individuals in these populations. The next work will compare the WMM results to concepts in existing clinical outcome assessments (COAs) used in AD trials. This study identifies the most commonly used measures in AD research throughout the disease continuum. Method Targeted literature and ClinicalTrials.gov searches identified COAs used in AD populations. Studies included pharmacological intervention or registry studies with individuals with preclinical or prodromal AD, mild cognitive impairment (MCI), mild, moderate, or severe AD, or caregivers of these individuals, and at least one type of COA. Results Over 900 records were retrieved. Records from the literature and Clinicaltrials.gov searches were combined and coded, yielding a total of 109 records for final review. Performance measures were the most common type of COA identified. COAs were most frequently used in MCI, and mild and moderate AD populations; COAs were used less often among prodromal/preclinical and severe AD populations. The six most frequently reported COAs identified were ADAS‐Cog, Mini‐Mental State Examination (MMSE), Neuropsychiatric Inventory (NPI), Alzheimer's Disease Cooperative Study ‐ Activities of Daily living Inventory (ADCS‐ADL), Clinical Dementia Rating Scale (CDR), and Alzheimer's Disease Cooperative Study ‐ Clinical Global Impression of Change (ADCS‐CGIC). Conclusion Performance measures (e.g., MMSE & CDR) were the most common type of COA identified, followed by clinician‐reported and observational measures. Only three COAs were used in all 5 AD populations: MMSE, ADCS‐ADL, NPI. Very few patient‐reported measures were identified; the most frequently being the EQ5D ( used in 4/5 AD populations). The most commonly used clinician‐reported measures included the NPI, ADCS‐CGIC, and Columbia Suicide Severity Rating Scale (C‐SSRS). The next phase of this work will assess the extent to which these existing measures capture the concepts that are most important to individuals with or at risk for AD and their care partners as identified in the WMM study.
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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 | 0.280 | 0.529 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.020 | 0.028 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".