Agitation in Alzheimer's disease: Novel outcome measures reflecting the International Psychogeriatric Association (IPA) agitation criteria
Bibliographic record
Abstract
INTRODUCTION: The 2017 European Union-North American Clinical Trials in Alzheimer's Disease Task Force recommended development of clinician-rated primary outcome measures for Alzheimer's disease (AD) agitation trials, incorporating International Psychogeriatric Association (IPA) criteria. METHODS: In a modified Delphi process, Cohen-Mansfield Agitation Inventory (CMAI) and Neuropsychiatric Inventory-Clinician (NPI-C) items were mapped to IPA agitation domains generating novel instruments, CMAI-IPA and NPI-C-IPA. Validation in the Agitation and Aggression AD Cohort (A3C) assessed minimal clinically important differences (MCIDs), change sensitivity, and predictive validity. RESULTS: MCID was -17 (odds ratio [OR] = 14.9, 95% confidence interval [CI] = 6.8-32.6) for CMAI; -5 (OR = 9.3, 95% CI = 4.0-21.2) for CMAI-IPA; -3 (OR = 11.9, 95% CI = 4.1-34.8) for NPI-C-A+A; and -5 (OR = 7.8, 95% CI = 3.4-17.9) for NPI-C-IPA at 3 months. Areas under the curve suggested no scale better predicted global clinician ratings. Sensitivity to change for all measures was high. CONCLUSION: Internal consistency and reliability analyses demonstrated better accuracy for the NPI-C-IPA than for the CMAI-IPA and can be used for agitation clinical trial inclusion, and for response to intervention.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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 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".