History and Accomplishments of the North American Clinical Trials Network for Spinal Cord Injury, 2004–2022
Bibliographic record
Abstract
This is a historical account of the origin and accomplishments of the North American Clinical Trials Network (NACTN) for traumatic spinal cord injury (SCI), which was established in 2004 by Christopher Reeve and Robert Grossman. Christopher Reeve was an actor who became quadriplegic and started the Christopher & Dana Reeve Foundation (CDRF), and Robert Grossman was a neurosurgeon experienced in neurotrauma and a university professor in Houston. NACTN has member investigators at university and military centers in North America and has contributed greatly to the improvement of care, primarily acute care, of patients sustaining traumatic SCI. Its accomplishments are a clinical registry database of >1000 acute SCI patients documenting the care pathways, including complications. NACTN has assessed the effectiveness of treatment, including pharmacotherapy and the role and timing of surgery, and has also identified barriers to early surgery. The principal focus has been on improving neurological recovery. NACTN has trained many SCI practitioners and has collaborated with other SCI networks and organizations internationally to promote the care of SCI patients.
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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 | 0.153 | 0.189 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".