Variability in the validity and reliability of outcome measures identified in a systematic review to assess treatment efficacy of cognitive enhancers for Alzheimer’s Dementia
Bibliographic record
Abstract
INTRODUCTION: Selection of optimal outcome measures is a critical step in a systematic review; inclusion of uncommon or non-validated outcome measures can impact the uptake of systematic review findings. Our goals were to identify the validity and reliability of outcome measures used in primary studies to assess cognition, function, behaviour and global status; and, to use these data to select outcomes for a systematic review (SR) on treatment efficacy of cognitive enhancers for Alzheimer's Dementia (AD). METHODS: Articles fulfilling the eligibility criteria of the SR were included in a charting exercise to catalogue outcome measures reported. Outcome measures were then assessed for validity and reliability. Two independent reviewers abstracted data on outcome measures and validity and reliability reported for cognition, function, behaviour and global status. RESULTS: 129 studies were included in the charting exercise; 57 outcome measures were identified for cognition, 21 for function, 13 for behaviour and 10 for global status. A total of 35 (61%) cognition measures, 10 (48%) functional measures, 8 (61%) behavioural measures and four (40%) of global status measures were only used once in the literature. Validity and reliability information was found for 51% of cognition measures, 90% of function and global status measures and 100% of behavioural measures. CONCLUSIONS: While a large number of outcome measures were used in primary studies, many of these were used only once. Reporting of validity and reliability varied in AD studies of cognitive enhancers. Core outcome sets should be used when available; when they are not available researchers need to balance frequency of reported outcome measures, their respective validity and reliability, and preferences of knowledge users. SYSTEMATIC REVIEW REGISTRATION: CRD#42012001948.
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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 | 0.272 | 0.640 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.015 |
| Bibliometrics | 0.022 | 0.023 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".