General Session 11: Clinial Trials/Respiratory
Bibliographic record
Abstract
Objective: The National Institute of Neurological Disorders and Stroke (NINDS) initiated development of spinal cord injury (SCI)-specific common data elements (CDE) as part of a project to develop data standards for funded clinical research in all fields of neuroscience.By developing these data standards for clinical research, the NINDS Spinal Cord Injury (SCI) CDE initiative strives to increase the efficiency and effectiveness of clinical research studies, increase data quality, facilitate data sharing, and help educate new clinical investigators.Design/Method: Working groups (WG) consisting of diverse experts are meeting regularly to develop a set of SCI-specific CDEs, selecting among, refining, and adding to existing, field-tested data elements from national registries and funded trials and studies.The composition of each WG includes clinical research experts in each disease area.Results: The first iteration of the NINDS SCI-specific CDEs will span 8 domains: (1) demographics, (2) care history/comorbidity, (3) functional, (4) electrodiagnostics, (5) participation/quality of life, (6) pain, (7) imaging, and (8) neurological.A CDE website provides uniform names and structures for each element, a data dictionary, and template case report forms using the CDEs.The latest information to be provided at this meeting will include the draft set of recommendations with examples of how the SCI CDEs may be used by a research study, demonstrations of navigating the NINDS CDE website and selecting SCI CDEs from it, and an explanation of how to submit feedback on the CDEs.Conclusion: The NINDS encourages involvement from the neurological clinical research community in this undertaking.The CDEs proposed for public review will be presented.The CDEs are an evolving resource with periodic reviews and updates based on feedback from the community and changes to the clinical research landscape.
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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 | 0.046 | 0.049 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.027 | 0.010 |
| Insufficient payload (model declined to judge) | 0.353 | 0.169 |
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".