Defining the Minimally Important Change for the SpondyloArthritis Research Consortium of Canada Spine and Sacroiliac Joint Magnetic Resonance Imaging Indices for Ankylosing Spondylitis
Bibliographic record
Abstract
OBJECTIVE: To define the minimally important change (MIC) in the SpondyloArthritis Research Consortium of Canada (SPARCC) spine and sacroiliac (SI) joint magnetic resonance imaging (MRI) indices in patients with ankylosing spondylitis. METHODS: MRI scans were performed during a placebo-controlled trial of adalimumab (no. NCT00195819). Two independent readers, blinded to treatment and sequence, determined SPARCC scores for the spine and SI joints and a global evaluation of change (GEC; "much worse," "worse," "no change," "better," or "much better"; categories other than "no change" were pooled together as "change") between baseline-Week 12, baseline-Week 52, and Weeks 12-52. Mean absolute changes in SPARCC scores (95% CI) were calculated for each interval, treatment group, and GEC. Receiver-operating characteristic (ROC) curves were used to identify the MIC. Relationships of MIC to clinical responses were examined. RESULTS: Reader agreement on GEC evaluations was > 70%. Changes in SPARCC scores were generally comparable between time intervals and treatment groups for "change" and "no change" categories and were combined for each category; change in score was significantly associated with GEC of "change" (area under ROC curves: spine 0.839; SI joints 0.960). ROC curves peaked at values of 5.0 for the spine and 2.5 for SI joints. Placebo-treated patients achieving > 2.5 unit improvement in SI joint score had significantly better clinical responses than placebo-treated patients who did not achieve such improvement. MRI and clinical responses were uncoupled in adalimumab-treated patients. CONCLUSION: We propose that changes of 5.0 for the spine and 2.5 for SI joints define the MIC for the SPARCC MRI indices.
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 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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".