Discriminating capacity of the ASAS health index in patients with axial spondyloarthritis treated with ixekizumab
Bibliographic record
Abstract
OBJECTIVE: To test the discriminating capacity of different thresholds of the Assessment of SpondyloArthritis international Society Health Index (ASAS HI) in placebo-controlled trials of patients with axial spondyloarthritis (axSpA), including radiographic (r-axSpA) and non-radiographic (nr-axSpA) subtypes. METHODS: The discriminating capacities of absolute (≥2.0-≥4.0 points) and relative (≥20%-≥50%) ASAS HI improvement thresholds were evaluated in patients with axSpA from three COAST trials (COAST-V, COAST-W, and COAST-X) of ixekizumab every 4 weeks (IXE Q4W) vs. placebo. Threshold-based response rates at Week 16 were compared between trial arms using Fisher's exact test. Odds ratios and phi coefficients were used to evaluate how strongly each improvement threshold was associated with treatment allocation in a given trial. Missing data were handled using non-responder imputation. RESULTS: ASAS HI data were available at baseline and Week 16 for 587 patients in IXE Q4W and placebo arms. The ASAS HI ≥30% improvement threshold effectively discriminated treatment allocation in all trials; significant differences were observed between IXE Q4W and placebo in r-axSpA (COAST-V: p = 0.026; COAST-W: p = 0.023) and nr-axSpA (COAST-X: p = 0.040). Lower absolute (≥2.0-≥3.0 points) and relative (≥20%-≥30%) thresholds discriminated effectively in COAST-W, whereas higher absolute (≥3.5-≥4.0 points) and relative (≥30%-≥50%) thresholds discriminated effectively in COAST-V. In COAST-X, ≥30%, ≥40%, and ≥50% thresholds discriminated effectively. Phi coefficients were small (<0.3) across all trials and thresholds. CONCLUSIONS: Several ASAS HI improvement thresholds discriminated axSpA patients in treatment vs. placebo arms at Week 16. The ASAS HI ≥30% improvement threshold discriminated across all three COAST trials.
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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 | 0.020 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".