International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI): A workbook with important classification cases
Bibliographic record
Abstract
STUDY PURPOSE: The International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) provides a widely accepted system for determining level and severity of a human spinal cord injury (SCI). The ISNCSCI is widely used for clinical purposes (communication of level and severity, monitor changes over time, establish rehabilitation goals and therapy programs and to predict neurological recovery on a group level) and in research (characterization, outcome measures as well as inclusion/exclusion criteria and (sub-)grouping criteria). Its successful application demands accuracy in both the examination and classification, of which the latter is the focus of this work. ISNCSCI classification involves precise rules and nuances, and inherent challenges have been described. The heterogeneity of SCI adds further complexity. A comprehensive dataset of representative ISNCSCI cases with annotated classifications is not yet available within the field. Therefore, the purpose of this dataset is to provide such a workbook to illustrate important classification rules, definitions, and nuances for a wide range of spinal cord injuries. DATA COLLECTED: Twenty-six hypothetical ISNCSCI cases were created by the authors to illustrate important classification rules, definitions, and nuances. Each case contains all 134 examined scores (2 body sides times 28 dermatomes light touch scores; 2 times 28 pin prick scores, 2 times 10 myotomes motor scores as well as voluntary anal contraction and deep anal pressure sensation) as well as all 11 classifications components: right and left sensory levels, right and level motor levels, neurological level of injury, completeness, American Spinal Injury Association (ASIA) Impairment Scale, right/left sensory zone of partial preservation, right/left motor zone of partial preservation. Each case additionally contains detailed explanations of the process for classifying each variable. The cases are documented and classified according to the eighth edition of the ISNCSCI revised in 2019 (https://doi.org/10.46292/sci2702-1). The cases cover a wide range of topics such as: - New ISNCSCI concepts introduced with the 2019 revision like the -- Non-SCI taxonomy for documentation of non-SCI related conditions superimposed to the SCI that may influence the examination of motor/sensory scores and impact the classification components (e.g., amputations, peripheral nerve lesions, pain, tendon transfers) -- Broadened ZPP applicability not only for sensorimotor complete, but also for a subset of incomplete lesions - Inherent classification challenges -- Motor incompleteness due to sparing of motor function more than three segments below the motor level -- Use of non-key muscle functions in the determination of motor incompleteness -- Motor levels in the high cervical and thoracic regions, where the motor level follows the sensory level -- The correct classification of levels, completeness and zones of partial preservation for ASIA Impairment Scale E classifications DATA USAGE NOTES:
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.013 |
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".