Development and Validation of an Algorithm for Item Reduction of the International Standards for Neurological Classification of Spinal Cord Injury Examination to Determine Level and Severity of SCI
Bibliographic record
Abstract
Background: In 2020, a first, expedited version of the International Standards for Neurological Classification of Spinal Cord Injury (E-ISNCSCI-V1) was proposed for determination of neurological level of injury (NLI) and American Spinal Injury Association Impairment Scale (AIS) classifications. Objectives: This work describes assessment of E-ISNCSCI-V1 classification accuracy and the development and data-based validation of an ISNCSCI Item Reduction Algorithm (IIRA). Methods: Classification accuracy for E-ISNCSCI-V1 examination shortcut options was assessed with automated analysis of 7026 full ISNCSCI examinations. Rules for the IIRA were iteratively adjusted to optimize the balance between omitting exam items and minimizing misclassification errors, and then it was validated through classification of 100 full ISNCSCI exams. Results: If S1 findings are substituted for anorectal exam findings as proposed for E-ISNCSCI-V1, the error rate for AIS is 10%, with a high error rate (45%) for classifying true AIS B. The IIRA, which begins with full motor testing, followed by limited sensory testing required an average of 31% (42/134) of the full ISNCSCI exam items, with a 2% error rate for NLI and no AIS errors. Conclusion: The previously proposed E-ISNCSCI-V1, which included an option to substitute S1 findings for anorectal exam findings, is not recommended due to AIS error rate. The IIRA provides a standardized option for a shortened examination classifying NLI and AIS with high accuracy. It will serve as a basis for version 2 of the E-ISNCSCI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".