Participant outcomes and preferences in Alzheimer's disease clinical trials: The electronic Person‐Specific Outcome Measure (ePSOM) development program
Bibliographic record
Abstract
INTRODUCTION: Current pharmacological interventions for Alzheimer's dementia delay symptom progression for about a year. Although the outcomes in earlier disease states may include changes in biomarkers, the clinical effectiveness of any intervention can ultimately only be assessed by a patient's self-reported well-being. A better understanding of earlier manifestations of Alzheimer's disease and the drive for relevant outcome measures, allied to technological advances in artificial intelligence, have mediated the electronic Person-Specific Outcome Measure (ePSOM) development program. METHODS: There are 4 sequential stages in the ePSOM development program-(1) literature review, (2) focus group study, (3) national survey, and (4) development of an app for capturing person-specific outcomes. Here, we report the overall approach to the program incorporating our literature review on patient-reported outcome measures and patient preferences in the Alzheimer's disease population. RESULTS: Alzheimer's disease trials do not use any patient-reported outcome measures. Quality of life measures are often used as proxies for this, but they do not capture individual needs. Therefore, trials currently fail to reflect the participant's aspirations for effect but rather default to clinicostatistical measure of cognition and function. There is no implementation of patient preferences despite evidence that understanding preferences may influence adherence to treatment. DISCUSSION: It is important to consider preferences for an intervention and use PROMs for the measure of effectiveness given that both risk and benefit are judged by the recipient of the treatment. The ePSOM development program will deliver the methodology for incorporating meaningful outcomes in clinical trials to expand upon current biological and clinical measurements of effectiveness.
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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 | 0.246 | 0.354 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".