Predicting Recruitment Feasibility for Acute Spinal Cord Injury Clinical Trials in Canada Using National Registry Data
Bibliographic record
Abstract
Traumatic spinal cord injury (SCI) represents a significant burden of illness, but it is relatively uncommon and heterogeneous, making it challenging to achieve sufficient subject enrollment in clinical trials of therapeutic interventions for acute SCI. The Rick Hansen Spinal Cord Injury Registry (RHSCIR) is a national SCI Registry that enters patients with SCI from acute-care centers across Canada. To predict the feasibility of conducting clinical trials of acute SCI within Canada, we have applied the inclusion/exclusion criteria of six previously conducted SCI trials to the RHSCIR data set and generated estimates of how many Canadian persons would have been eligible theoretically for enrollment in these studies. Data for SCI cases were prospectively collected for RHSCIR at 18 acute and 13 rehabilitation sites across Canada. RHSCIR patients enrolled between 2009-2013 who met the following key criteria were included: non-penetrating traumatic SCI; received acute care at a RHSCIR site; age more than 18, less than 75 years, and had complete admission single neurological level of injury data. Inclusion and exclusion criteria for the Minocycline in Acute Spinal Cord injury (Minocycline), Riluzole, Surgical Timing in Acute Spinal Cord Injury Study (STASCIS), Cethrin, Nogo antibody study (NOGO), and Sygen studies were applied retrospectively to this data set. The numbers of patients eligible for each clinical trial were determined. There were 2166 of the initial 2714 patients (79.8%) who met the key criteria and were included in the data set. Projected annual numbers of eligible patients for each trial were: Minocycline, 117; Riluzole, 62; STASCIS, 109; Cethrin, 101; NOGO, 82; and Sygen, 70. An additional 8.0% of the sample had a major head injury (Glasgow Coma Scale [GCS] score ≤12) and would have been excluded from the trials. RHSCIR provides a comprehensive national data set that may serve as a useful tool in the planning of multicenter clinical SCI trials.
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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 | 0.143 | 0.287 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".