The Spinal Cord Independence Measure (SCIM) version III: Reliability and validity in a multi-center international study
Bibliographic record
Abstract
PURPOSE: To examine the third version of the Spinal Cord Independence Measure (SCIM III) for reliability and validity in a multi-center cohort study. METHOD: Four hundred and twenty-five patients with spinal cord lesions from 13 spinal cord units in six countries from three continents were assessed with SCIM III and the Functional Independence measure (FIM) on admission to rehabilitation and before discharge. RESULTS: Total agreement between raters was above 80% in most SCIM III tasks, and all kappa coefficients were statistically significant (P<0.001). The coefficients of Pearson correlation between the paired raters were above 0.9, and intraclass correlation coefficients were above 0.94. Cronbach's alpha was above 0.7. The coefficient of Pearson correlation between FIM and SCIM III was 0.790 (P<0.01). SCIM III was more responsive to changes than FIM in the subscales of Respiration and sphincter management and Mobility indoors and outdoors. CONCLUSIONS: The results support the reliability and validity of SCIM III in a multi-cultural setup. Despite several limitations of the study, the results indicate that SCIM III is an efficient measure for functional assessment of SCL patients and can be safely used for clinical and research trials, including international multi-center 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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".