Assessment of Clinical Factors that Predict Response to Nabilone for Agitation in Alzheimer's Disease: A Post Hoc Analysis of a Randomized Control Trial
Bibliographic record
Abstract
BACKGROUND: Agitation is one of the most prevalent neuropsychiatric symptoms (NPS) of Alzheimer's disease (AD). A crossover trial of nabilone for agitation in AD found that nabilone was effective in treating agitation. We aimed to identify which clinical characteristics predicted response to nabilone intervention for agitation. METHOD: Twenty-two potential clinical characteristics were identified a priori. Characteristics were analyzed for their ability to predict improvements on the Cohen-Mansfield Agitation Inventory (CMAI). Each characteristic was categorically split and compared with univariate analyses: characteristics showing differences ≥8 CMAI points were included in a multivariate regression to model interactions between the treatment and characteristics. Index scores were calculated to represent the likelihood of response to treatment and results were grouped into quartiles. Multicollinearity of the characteristics was assessed. RESULT: 35 patients (28 males (80%), mean age [SD] 87.0 [10.2] years, CMAI 67.4 [17.7], standardized Mini-Mental State Exam (sMMSE) 6.6 [6.8]) had complete data for clinical predictors, allowing for 70 cases to be analyzed. Six predictors met criteria for inclusion in multivariate modelling, where nabilone was more effective in participants with sMMSE scores ≥6 (Δ level estimates = -12.0), higher levels of pain (-17.1), apathy (-8.7), appetite and eating changes (-9.7), and not taking cholinesterase inhibitors (ChEIs) (-8.4). CONCLUSION: AD patients with pain, apathy and appetite changes, and with less cognitive impairment and not on ChEIs, were most likely to benefit from nabilone. If replicated with phase 3 data, these predictors may assist in guiding clinicians on who is likely to benefit from nabilone when managing agitation.
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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 | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".