Common data elements for clinical research in Friedreich's ataxia
Bibliographic record
Abstract
To reduce study start-up time, increase data sharing, and assist investigators conducting clinical studies, the National Institute of Neurological Disorders and Stroke embarked on an initiative to create common data elements for neuroscience clinical research. The Common Data Element Team developed general common data elements, which are commonly collected in clinical studies regardless of therapeutic area, such as demographics. In the present project, we applied such approaches to data collection in Friedreich's ataxia (FRDA), a neurological disorder that involves multiple organ systems. To develop FRDA common data elements, FRDA experts formed a working group and subgroups to define elements in the following: ataxia and performance measures; biomarkers; cardiac and other clinical outcomes; and demographics, laboratory tests, and medical history. The basic development process included identification of international experts in FRDA clinical research, meeting by teleconference to develop a draft of standardized common data elements recommendations, vetting of recommendations across the subgroups, and dissemination of recommendations to the research community for public comment. The full recommendations were published online in September 2011 at http://www.commondataelements.ninds.nih.gov/FA.aspx. The subgroups' recommendations are classified as core, supplemental, or exploratory. Template case report forms were created for many of the core tests. The present set of data elements should ideally lead to decreased initiation time for clinical research studies and greater ability to compare and analyze data across studies. Their incorporation into new, ongoing studies will be assessed in an ongoing fashion to define their utility in FRDA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".