Clinical Data Interchange Standards in Clinical Trials on Alzheimer’s Disease
Bibliographic record
Abstract
OBJECTIVE: The Clinical Data Interchange Standards Consortium (CDISC) proposed outcome measures for clinical trials on Alzheimer's disease (AD) in the Therapeutic Area User Guide for AD (TAUG-AD). To investigate how well the clinical trials on AD registered in the ClinicalTrials.gov complied with the recommendations on outcome measures by the CDISC. METHODS: We compared the outcome measures proposed in the TAUG-AD version 2.0.1 with those employed in the protocols of clinical trials on AD registered in ClinicalTrials.gov. RESULTS: We analyzed 101 outcome measures from 305 protocols. The TAUG-AD listed ten scales for outcome measures of clinical trials on AD. The scales for cognition, activities of daily living, behavioral and psychological symptoms of dementia, and global severity listed in TAUG-AD were most frequently employed in the clinical trials on AD. However, TAUG-AD did not include any scale on quality of life. Also, several scales such as Montreal Cognitive Assessment, Alzheimer's Disease Cooperative Study-Activities of Daily Living, and Cohen- Mansfield Agitation Inventory not listed in the TAUG-AD were commonly employed in the clinical trials on AD and changed over time. CONCLUSION: To properly standardize the data from clinical trials on AD, the gap between the TAUG-AD and the measures employed in real-world clinical trials should be filled.
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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 | 0.802 | 0.835 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.021 | 0.038 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.016 | 0.015 |
| Research integrity | 0.018 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".