The Reproducibility and Convergent Validity of the Walking Index for Spinal Cord Injury (WISCI) in Chronic Spinal Cord Injury
Bibliographic record
Abstract
BACKGROUND: The Walking Index for Spinal Cord Injury II (WISCI II) is a hierarchical scale that measures improvements in walking following spinal cord injury (SCI). The WISCI II has good face validity, concurrent validity, and reliability following acute SCI; however, psychometric properties need to be determined for chronic SCI. Because prior studies have demonstrated a relationship between lower-extremity motor scores (LEMS) and walking, outcome measures for walking should demonstrate a linkage between the underlying impairment (weakness) and walking-convergent validity. OBJECTIVE: To determine convergent validity and reproducibility of the WISCI II. METHODS: Self-selected and maximum WISCI levels were assessed for 76 patients with chronic SCI (34 paraplegia, 42 tetraplegia); 10-m walking speeds were calculated. Convergent validity was assessed by correlating WISCI II levels to LEMS and walking speed. Reproducibility was assessed with the intraclass correlation coefficient (ICC) and the smallest real difference (SRD). RESULTS: Convergent validity of the self-selected and maximum WISCI II with LEMS was moderate for paraplegia (ρ = 0.479 and ρ = 0.533) and strong for tetraplegia (ρ = 0.852 and ρ = 0.816). Tetraplegia, but not paraplegia, demonstrated convergent validity of walking speed at the self-selected and maximum WISCI levels with LEMS (ρ = 0.752 and ρ = 0.813). WISCI reproducibility was excellent (self-selected ICC = 0.994; maximum ICC = 0.995), resulting in SRDs of 0.785 (self-selected) and 0.597 (maximum), suggesting that a change of one WISCI level can be interpreted as real in a chronic patient. CONCLUSIONS: Results suggest that the WISCI II should be a very useful outcome measure for detecting changes in walking function following chronic SCI.
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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 | 0.020 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".