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Record W2439833245 · doi:10.3899/jrheum.150439

Instrumented BASFI (iBASFI) Shows Promising Reliability and Validity in the Assessment of Activity Limitations in Axial Spondyloarthritis

2016· article· en· W2439833245 on OpenAlexvenueno aff
Thijs Willem Swinnen, Milica Milošević, Sabine Van Huffel, Wim Dankaerts, René Westhovens, Kurt de Vlam

Bibliographic record

VenueThe Journal of Rheumatology · 2016
Typearticle
Languageen
FieldMedicine
TopicSpondyloarthritis Studies and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsBASFIBASDAIMedicineAnkylosing spondylitisPhysical therapyReliability (semiconductor)Construct validityCriterion validityAccelerometerPhysical medicine and rehabilitationAlgorithmComputer sciencePsychometricsSurgeryPathologyClinical psychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.052
GPT teacher head0.310
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2016
Admission routes1
Has abstractyes

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