Clarifications of the Motor Level Definition in the International Standards for Neurological Classification of Spinal Cord Injury in Not Clinically Testable Myotomes
Bibliographic record
Abstract
Background: In the International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI), two approaches for determining motor levels (MLs) in not clinically testable myotomes (C2-C4, T2-L1, S2-S5) are described: one where the motor level follows the sensory level (MFSL) and another deriving motor function from sensory function (MFSF). Their results differ when (1) all key muscles of an upper (or upper and lower) extremity are scored as intact, (2) sensation is not normal in key muscle segments, and (3) a contiguous region of normal sensation starts at T2 (or S2). Objectives: This work aims to characterize these cases and to discuss explanations. Methods: We analyzed 1330 early and late ISNCSCI assessments of 665 individuals from EMSCI. Results: Forty-nine (3.6% of all 2660 MLs) MFSL (63.3% T1, 36.7% S1) and MFSF MLs from 34 individuals differed without consequences on ASIA Impairment Scale (AIS) grades (4 AIS A, 1 AIS B, 29 AIS D). In 16 AIS D cases, all testable motor functions were intact, with a mean Spinal Cord Independence Measure (SCIM) total score of 95.67 ± 3.51 in 3 individuals with MFSL-ML T1 and 100 in 5 individuals with MFSL-ML S1. The MFSF-MLs are on average 9.63 ± 7.50 (T1: 12.16 ± 8.43; S1: 5.28 ± 1.36) segments caudal to the sensory level (SL). Conclusion: We identified and characterized rare cases with an unusual sensory impairment pattern, which could be explained by an isolated damage of afferent spinal tracts or the presence of non-SCI conditions. Further investigations of these case are necessary for a more conclusive ML definition.
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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 | 0.090 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".