Comparison of three methods for calculating the Bath Ankylosing Spondylitis Metrology Index in a randomized placebo‐controlled study
Bibliographic record
Abstract
OBJECTIVE: To compare 3 methods for calculating the Bath Ankylosing Spondylitis Metrology Index (BASMI) score using patients who participated in the GO-RAISE study. METHODS: Patients with active ankylosing spondylitis (AS) were randomly assigned in a 1:1.8:1.8 ratio to receive subcutaneous injections of placebo, golimumab 50 mg, or golimumab 100 mg every 4 weeks. Lumbar flexion, tragus-to-wall distance, lumbar side flexion, intermalleolar distance, and cervical rotation angle measurements at baseline, week 14, and week 24 were used to calculate the BASMI 2-step (BASMI(2) ), 10-step (BASMI(10) ), and linear (BASMI(lin) ) scores. RESULTS: BASMI(2) scores were generally lower than BASMI(10) and BASMI(lin) scores, which were nearly identical. Median changes from baseline to week 14 in the combined golimumab group were similar to those in the placebo group when using the BASMI(2) calculation method (0.00 versus 0.00; P = 0.288). The combined golimumab group showed significantly greater improvement from baseline to week 14 than the placebo group when using the BASMI(10) (-0.20 versus 0.00; P = 0.018) and BASMI(lin) (-0.31 versus -0.07; P = 0.015) calculation methods, with the latter showing the greatest difference between golimumab and placebo. Guyatt's effect size was better for the BASMI(lin) and the BASMI(10) versus the BASMI(2) in the combined golimumab group at week 14 (0.58 and 0.53 versus 0.42, respectively) and week 24 (0.76 and 0.69 versus 0.61, respectively), despite the relatively short period to assess changes in spinal mobility. CONCLUSION: The BASMI(lin) method was the most sensitive to changes in range of motion exhibited by patients with AS who received golimumab.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".