A New Score Based on the International Standards for Neurological Classification of Spinal Cord Injury for Integrative Evaluation of Changes in Sensorimotor Functions
Bibliographic record
Abstract
Sensorimotor function of patients with spinal cord injury (SCI) is commonly assessed according to the International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI). From the ISNCSCI segmental motor and sensory assessments, upper and lower extremity motor scores (UEMS and LEMS), sum scores of pinprick (PP) and light touch (LT) sensation, the neurological level of injury (NLI) and the classification of lesion severity according to the American Spinal Injury Association Impairment Scale (AIS) grade are derived. Changes of these parameters over time are used widely to evaluate neurological recovery. Evaluating recovery based on a single ISNCSCI scoring or classification variable, however, may misestimate overall recovery. Here, we propose an Integrated Neurological Change Score (INCS) based on the combination of normalized changes between two time points of UEMS, LEMS, and total PP and LT scores. To assess the agreement of INCS with clinical judgment of meaningfulness of neurological changes, changes of ISNCSCI variables between two time points of 88 patients from an independent cohort were rated by 20 clinical experts according to a five-categories Likert Scale. As for individual ISNCSCI variables, neurological change measured by INCS is associated with severity (AIS grade), age, and time since injury, but INCS better reflects clinical judgment about meaningfulness of neurological changes than individual ISNCSCI variables. In addition, INCS is related to changes in functional independence measured by the Spinal Cord Independence Measure (SCIM) in patients with tetraplegia. The INCS may be a useful measure of overall neurological change in clinical studies.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".