A taxonomy for consistent handling of conditions not related to the spinal cord injury (SCI) in the International Standards for Neurological Classification of SCI (ISNCSCI)
Bibliographic record
Abstract
STUDY DESIGN: Committee consensus process including additional structured feedback from spinal cord injury (SCI) experts attending a focus group workshop. OBJECTIVES: To define a taxonomy for standardized documentation of non-SCI-related conditions in the International Standards for Neurological Classification of SCI (ISNCSCI). SETTING: Americal Spinal Injury Association (ASIA) International Standards Committee with 16 international ISNCSCI experts. METHODS: With the new taxonomy, not-normal sensory or motor scores should be tagged with an asterisk ("*"), if they are impacted by a non-SCI condition such as burns, casts, joint contractures, peripheral nerve injuries, amputations, pain, or generalized weakness. The non-SCI condition and instructions on how to handle the "*"-tagged scores during classification should be detailed in the comments box. While sum scores are always calculated based on examined scores, classification variables such as the neurological level of injury (NLI) or the ASIA Impairment Scale (AIS) grades are tagged with an "*", when they have been determined on the basis of clinical assumptions. RESULTS: With the extended "*"-tag concept, sensory and motor examination results impacted by non-SCI conditions above, at, or below the NLI can be consistently documented, scored, and classified. Feedback from workshop participants confirms agreement on its clinical relevance, logic and soundness, easiness of understanding, communicability, and applicability in daily work. CONCLUSIONS: After multiple internal revisions, a taxonomy for structured documentation of conditions superimposed on the impairments caused by the SCI together with guidelines for consistent scoring and classification was released with the 2019 ISNCSCI revision. This taxonomy is intended to increase the accuracy of ISNCSCI classifications.
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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 | 0.347 | 0.321 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.004 | 0.010 |
| 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".