Spinal Cord Injury Functional Ambulation Profile
Bibliographic record
Abstract
BACKGROUND: Objective measures of functional walking remain scarce for individuals with incomplete spinal cord injury (ISCI). Hence, the authors developed the Spinal Cord Injury Functional Ambulation Profile (SCI-FAP), which encompasses the timed performance of 7 tasks, such as walking and negotiating obstacles, doors, and stairs. OBJECTIVE: To assess the reliability and validity of the SCI-FAP. METHODS: A total of 32 individuals with ISCI attended 2 testing sessions separated by 1 to 2 weeks. At the first session, participants performed the SCI-FAP, the 10-m walk test (10MW), the 6-minute walk test (6MW), and the Walking Index for Spinal Cord Injury (WISCI II); 3 raters scored the participants' performances on the SCI-FAP to assess interrater reliability. To establish convergent validity, SCI-FAP scores were compared with scores on the 10MW, 6MW, and WISCI II. At the second session, participants completed the SCI-FAP to assess test-retest reliability. To establish discriminative validity, SCI-FAP scores were compared between participants with ISCI and 60 able-bodied adults, who were scored on the SCI-FAP in a single session. RESULTS: Internal consistency (α = .95), interrater reliability (intraclass correlation coefficient [ICC] = 1.00), and test-retest reliability (ICC = 0.98) of the SCI-FAP were high. SCI-FAP scores were moderately correlated with results from the 10MW (r = -0.59), 6MW (r = -0.59), and WISCI II (M = -0.68). Performance on the SCI-FAP was significantly poorer among participants with ISCI compared with their able-bodied counterparts (P = .002). CONCLUSIONS: The findings support the SCI-FAP as a valid and reliable measure of walking skill for individuals with ISCI.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".