International Standards for Neurological Classification of Spinal Cord Injury: Case Examples Reinforcing Concepts From the 2019 Revision
Bibliographic record
Abstract
Background: The International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) is the most widely accepted system for characterizing sensorimotor impairments after spinal cord injury (SCI). There have been a number of ISNCSCI revisions, with the most recent edition published in 2019. Newer concepts, including the revised definitions of the zones of partial preservation (ZPPs) and documentation of non-SCI conditions, require training and practice for successful utilization. The International Standards Committee developed an ISNCSCI workbook of 26 practice cases, each with detailed explanations of the correct classification components. In this article, we present seven cases, which were selected from the workbook to reinforce the changes implemented in 2019. Methods: Hypothetical ISNCSCI cases were created to illustrate important classification rules, definitions, and nuances. All cases were reviewed by members of the American Spinal Injury Association (ASIA) International Standards Committee, and if any discrepancies were identified, they were discussed until a consensus was reached. To confirm agreement, cases were also entered into online algorithms, which are compliant with the 2019 ISNCSCI revision. The seven cases in this article highlight newer classification concepts and include a discussion of key elements. Cases: Each case reinforces the revised definitions of the ZPPs, such as the applicability of sensory ZPPs in all injuries without sensory sacral sparing and applicability of motor ZPPs in all injuries without voluntary anal contraction (VAC). Non-SCI-related impairments and their impact on the classification are reviewed in Cases 4-7. Conclusion: The seven cases presented in this article feature key concepts from the 2019 ISNCSCI revision. These cases, as well as the full ISNCSCI workbook, can serve as valuable training tools to improve classification accuracy.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".