Bibliographic record
Abstract
The successful execution of clinical trials for novel anti-rheumatic compounds is increasingly approaching the limits of what can be achieved using radiographic outcomes for the assessment of disease modification. Moreover, there is a growing need for more objective tools to assess joint inflammation, especially for disorders such as axial spondyloarthritis where spinal symptoms are often non-specific and physical findings may be minimal until later stages of disease. The use of MRI to evaluate inflammation in the synovium and bone marrow as well as erosions in peripheral joints of patients with RA and PsA represents a major new advance that should now be routinely implemented in clinical trials of RA. MRI-based scoring systems have been well validated and demonstrate that, for RA, MRI changes after therapeutic intervention may be observed in a month and precede findings on radiography that only become evident after a year. The assessment of disease activity on MRI of the sacroiliac joints and spine using a standardized and well-validated method, such as the SPARCC instruments, is indispensable to the evaluation of efficacy for new agents aimed at the treatment of spondyloarthritis. Further advances include the use of whole-body MRI evaluation to assess inflammation in both the axial and peripheral skeleton as well as sequences that dispense with the requirement for the use of contrast agents, such as gadolinium, and data processing techniques that permit full automation and absolute quantification. This review will discuss how imaging is transforming clinical trials in rheumatic diseases.
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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 | 0.062 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".