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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".