Clinical Outcome Measure Crosswalks in Alzheimer’s Disease: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: A key challenge in studies that model outcomes, disease progression, and cost-effectiveness of existing and emerging dementia treatments is the lack of conversion criteria to translate, or 'crosswalk', scores on multiple measurement scales. Clinical status in dementia is commonly characterized in the cognitive, functional, and behavioral domains. OBJECTIVE: We conducted a systematic review of peer-reviewed dementia measure crosswalks in the three domains. METHODS: We systematically reviewed published literature for crosswalks between scales used to measure cognitive, functional, or behavioral outcomes in Alzheimer's and related dementias. The search was conducted in PubMed, and additional crosswalks were identified through snowballing and expert input from dementia modelers. RESULTS: Of the reviewed articles, 2,334 were identified through a PubMed search, 842 articles were sourced from backward and forward citation snowballing, and 8 additional articles were recommended through expert input. 31 papers were eligible for inclusion, listing 74 unique crosswalks. Of those, 62 (83.8%) were between endpoints of the cognitive domain and 12 (16.2%) were either between endpoints of the functional domain or were hybrid in nature. Among crosswalks exclusively in the cognitive domain, a majority involved the Mini-Mental State Examination (MMSE) (37 crosswalks) or the Montreal Cognitive Assessment (MoCA) and its variants (25 crosswalks). MMSE was directly compared to MoCA or MoCA variants in 16 crosswalks. CONCLUSION: Existing crosswalks between measures of dementia focus largely on a limited selection of outcome measures, particularly MMSE and MoCA. Few crosswalks exist in the functional domain, and no crosswalks were identified for solely behavioral measures.
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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 | 0.033 | 0.145 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".