Validation of a New Tool to Detect and Characterize Spinal Cord Injury in the Acute Trauma Patient: The Montreal Acute Classification of Spinal Cord Injury
Bibliographic record
Abstract
STUDY DESIGN: Retrospective validation protocol. OBJECTIVE: The International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) is the most comprehensive tool for classifying spinal cord injuries (SCI), but it is not adapted for the evaluation of trauma patients. The objective is to develop and validate a streamlined tool, the Montreal Acute Classification of Spinal Cord Injury (MAC-SCI) that can be integrated in the evaluation of trauma patients to detect and characterize traumatic SCI (tSCI). METHODS: The completion rate of the ISCNSCI during initial evaluation after tSCI was estimated at a Level-1 trauma center specialized in SCI care. Using a modified Delphi technique, we designed the MAC-SCI, a new tool to detect and characterize the severity grade and level of SCI in the polytrauma patient. A cohort consisting of 35 consecutive tSCI patients with complete ISNCSCI documentation was used to validate the MAC-SCI. The severity grade and neurological level of injury (NLI) were assessed using the MAC-SCI, and compared to those obtained with the ISNCSCI. RESULTS: Only 33% of 148 patients admitted after a tSCI had a complete ISNCSCI performed at initial presentation. The MAC-SCI retains 53 of the 134 elements from the ISNCSCI. There was a 100% concordance in severity grade between the MAC-SCI and ISNCSCI. The NLI were within 2 levels between the MAC-SCI and ISNCSI for 100% of patients. CONCLUSION: The MAC-SCI is a streamlined tool that accurately detects and characterizes tSCI in the acute trauma setting. It could be implemented in trauma protocols to guide the management of SCI patients. LEVEL OF EVIDENCE: Level III Diagnostic criteria.
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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 | 0.045 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".