A common data language for clinical research studies: the National Institute of Neurological Disorders and Stroke and American Academy for Cerebral Palsy and Developmental Medicine Cerebral Palsy Common Data Elements Version 1.0 recommendations
Bibliographic record
Abstract
To increase the efficiency and effectiveness of clinical research studies, cerebral palsy (CP) specific Common Data Elements (CDEs) were developed through a partnership between the National Institute of Neurological Disorders and Stroke (NINDS) and the American Academy of Cerebral Palsy and Developmental Medicine (AACPDM). International experts reviewed existing NINDS CDEs and tools used in studies of children and young people with CP. CDEs were compiled, subjected to internal review, and posted online for external public comment in September 2016. Guided by the International Classification of Functioning, Disability and Health framework, CDEs were categorized into six domains: (1) participant characteristics; (2) health, growth, and genetics; (3) neuroimaging; (4) neuromotor skills and functional assessments; (5) neurocognitive, social, and emotional assessments; and (6) engagement and quality of life. Version 1.0 of the NINDS/AACPDM CDEs for CP is publicly available on the NINDS CDE and AACPDM websites. Global use of CDEs for CP will standardize data collection, improve data quality, and facilitate comparisons across studies. Ongoing collaboration with international colleagues, industry, and people with CP and their families will provide meaningful feedback and updates as additional evidence is obtained. These CDEs are recommended for NINDS-funded research for CP. WHAT THIS PAPER ADDS: This is the first comprehensive Common Data Elements (CDEs) for children and young people with CP for clinical research. The CDEs for children and young people with CP include common definitions, the standardization of case report forms, and measures. The CDE guides the standardization for data collection and outcome evaluation in all types of studies with children and young people with CP. The CDE ultimately improves data quality and data sharing.
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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 | 0.341 | 0.694 |
| Meta-epidemiology (narrow) | 0.004 | 0.010 |
| Meta-epidemiology (broad) | 0.015 | 0.015 |
| Bibliometrics | 0.030 | 0.039 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.028 | 0.020 |
| Open science | 0.015 | 0.026 |
| Research integrity | 0.022 | 0.052 |
| Insufficient payload (model declined to judge) | 0.050 | 0.059 |
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".