Measuring clinically relevant change in apathy symptoms in ADMET and ADMET 2
Bibliographic record
Abstract
OBJECTIVES: Among participants with Alzheimer's disease (AD) we estimated the minimal clinically important difference (MCID) in apathy symptom severity on three scales. DESIGN: Retrospective anchor- and distribution-based analyses of change in apathy symptom scores. SETTING: Apathy in Dementia Methylphenidate Trial (ADMET) and ADMET 2 randomized controlled trials conducted at three and ten clinics specialized in dementia care in United States and Canada, respectively. PARTICIPANTS: Two hundred and sixty participants (60 ADMET, 200 ADMET 2) with clinically significant apathy in Alzheimer's disease. MEASUREMENTS: The Clinical Global Impression of Change in Apathy scale was used as the anchor measure and the MCID on the Neuropsychiatric Inventory - Apathy (NPI-A), Dementia Apathy Interview and Rating (DAIR), and Apathy Evaluation Scale-Informant (AES-I) were estimated with linear mixed models across all study visits. The estimated thresholds were evaluated with performance metrics. RESULTS: Among the MCID was a decrease of four points (95% CI: -4.0 to -4.8) on the NPI-A, 0.56 points (95% CI: -0.47 to -0.65) on the DAIR, and three points on the AES-I (95% CI: -0.9 to -5.4). Distribution-based analyses were largely consistent with the anchor-based analyses. The MCID across the three measures showed ∼60% accuracy. Sensitivity analyses found that MMSE scores and apathy severity at baseline influenced the estimated MCID. CONCLUSIONS: MCIDs for apathy on three scales will help evaluate treatment efficacy at the individual level. However, the modest correspondence between MCID and clinical impression of change suggests the need to consider other scales.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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) | 0.001 | 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".