An Evaluation of International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) Performance Within the Canadian SCI Network
Bibliographic record
Abstract
Objectives: To describe the performance of the International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) examination in individuals with traumatic spinal cord injury (TSCI) and nontraumatic spinal cord injury (NTSCI) across Canadian acute and rehabilitation facilities, evaluating timing, completeness, and classification accuracy. Methods: Using the Rick Hansen Spinal Cord Injury Registry (2015-2022), participants were analyzed across 6 cohorts: (A) TSCI-acute-admission ( n = 4461), (B) TSCI-acute-discharge ( n = 972), (C) TSCI-rehabilitation-admission ( n = 2673), (D) TSCI-rehabilitation-discharge (n = 2316), (E) NTSCI-rehabilitation-admission ( n = 728), and (F) NTSCI-rehabilitation-discharge (n = 619). ISNCSCI data included performed (yes/no), timing (≤72 hours, ≤7 days, and >7 days of admission/discharge), completeness, missing items, and worksheet used (yes/no). Classification accuracy between the clinician-determined and algorithm-generated ASIA Impairment Scale and neurological level of injury classification was evaluated. Descriptive and bivariate statistics were used to analyze cohorts. Results: Overall, 70% of participants had at least one examination performed, with 76% performed ≤72 hours, 91% ≤7 days, and 9% >7 days. However, 45% were partially complete, primarily missing sensory scores and rectal components ≤7 days. Comparison of TSCI and NTSCI during rehabilitation showed that NTSCI cohorts had significantly more exams at admission and fewer at discharge, with more complete exams. Moreover, age at injury, injury type, mechanism, severity, length of stay, and pain influenced examination performance. Conclusion: This study highlights the need for greater consistency in ISNCSCI examination performance and identifies patient-level barriers to completion. Determining the most effective standardized approach for ISNCSCI use across SCI care, addressing modifiable human/organizational factors, and ensuring comprehensive clinical training will improve the quality of this assessment.
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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 | 0.023 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".