Potential North American Clinical Trials Network (NACTN) for Treatment of Spinal Cord Injury: A Consortium of Military, Veterans Administration, and Civilian Hospitals
Bibliographic record
Abstract
The first military (WRAMC) and four new civilian hospitals have joined NACTN. 198 patients were enrolled in national data registry; NACTN PIs are analyzing the data and preparing a manuscript on the occurrence of acute injury complications. NACTN has received ORP approval for the data registry protocol and the NACTN Data Management Center has expanded to accommodate increasing patient numbers. Modifications submitted to TATRC include: Stemnion (approved and project underway); FY 2007 and riluzole mods (the latter to replace the originally proposed anti-Nogo study) are in the review/contracting continuum. In anticipation of approval, all NACTN personnel met in February 2008 to discuss the riluzole protocol/safety study, and the centers are working with local IRBs and ORP to fulfill all regulatory requirements for the data registry. The final riluzole protocol will be submitted to the ORP HRPO for HSRRB review after securing IRB approval from one NACTN site. National ASIA training for all clinical NACTN personnel will be held in Louisville June 2-3, 2008. NACTN is collaborating with three other clinical networks: the European Union Clinical Trial Network, the Canadian SCI Translational Research Network and NIH-funded NETT. GRASSP validation is nearing successful completion and preliminary STASCIS data suggest that early decompression of the spinal cord (< 24h) is associated with improved neurological recovery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.066 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.008 |
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".