Instrumented BASFI (iBASFI) Shows Promising Reliability and Validity in the Assessment of Activity Limitations in Axial Spondyloarthritis
Bibliographic record
Abstract
OBJECTIVE: The Bath Ankylosing Spondylitis Functional Index (BASFI) is the most popular method to assess activity capacity in axial spondyloarthritis (axSpA), to our knowledge. It is endorsed by the Assessment of Spondyloarthritis international Society. But it may have recall bias or aberrant self-judgments in individual patients. Therefore, we aimed to (1) develop the instrumented BASFI (iBASFI) by adding a body-worn accelerometer with automated algorithms to performance-based measurements (PBM), (2) study the iBASFI's core psychometric properties, and (3) reduce the number of iBASFI items. METHODS: Twenty-eight patients with axSpA wore a 2-axial accelerometer while completing 12 PBM derived from the BASFI. A chronometer and both manual and "automated algorithm-based" acceleration segmentation identified movement time. Test-retest trials and methods (algorithm vs manual segmentation/chronometer/BASFI) were compared with ICC, standard error of measurement [percentage of movement time (SEM%)], and Spearman ρ correlation coefficients. Linear regression identified the optimal set of reliable iBASFI PBM. RESULTS: Good to excellent test-retest reliability was found for 8/12 iBASFI items (ICC range 0.812-0.997, SEM range 0.4-30.4%), typically with repeated and fast movements. Automated algorithms excellently mimicked manual segmentation (ICC range 0.900-0.998) and the chronometer (ICC range 0.878-0.998) for 10/12 iBASFI items. Construct validity compared with the BASFI was confirmed for 7/12 iBASFI items (δ range 0.504-0.755). Together, sit-to-stand speed test (stBeta 0.483), cervical rotation (stBeta -0.392), and height (stBeta -0.375) explained 59% of the variance in the BASFI (p < 0.01). CONCLUSION: The proof-of-concept iBASFI showed promising reliability and validity in measuring activity capacity. The number of the iBASFI's PBM may be minimized, but further validation in larger axSpA cohorts is needed before its clinical use.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".