PT594. Placebo effects in Alzheimer Disease: analysis of the CATIE-AD data
Bibliographic record
Abstract
Abstract Background: Differences in symptom trajectories of behavioral and psychological symptoms with dementia (BPSD) between placebo and active drug responders have not been investigated. Additionally, ideal criteria for screening of potential placebo responders in a placebo lead-in phase in clinical trials for BPSD remain unknown. Methods: The data of 371 patients with Alzheimer disease (DSM-IV) in Phase 1 of the Clinical Antipsychotic Trials of Intervention Effectiveness for Alzheimer disease (The CATIE-AD) were analyzed. The patients were randomly assigned to treatment with olanzapine, quetiapine, risperidone, or placebo in a double-blind condition. Trajectories of Brief Psychiatric Rating Scale (BPRS) total scores were compared between placebo and active drug responders (i.e. those who achieved a ≥25% BPRS total score reduction). Prediction performance of binary classification in improvement at week 2 for placebo response at week 8 was examined; sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the consecutive cut-off points in increments of 5% between 5% and 25% in the BPRS total score reduction at week 2 were calculated. Results: There were no significantly differences in symptom trajectories between placebo and drug groups. The cut-off of 10% at week 2 presented with the highest precision of 0.66 with sensitivity, NPV, specificity, and PPV of 0.53, 0.65, 0.77, and 0.68, respectively. Conclusion: Symptom trajectories of BPSD in responders follow a similar pattern irrespective of treatment modalities. The 10% cut-off at week 2 seems robust for the prediction of subsequent placebo response at week 8, which may need to be considered in future clinical trials to reduce failure trials for BPSD.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.008 | 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".