Spondyloarthritis. Clinical Versus Imaging Assessment: And the Winner Is?
Bibliographic record
Abstract
Spondyloarthritis (SpA) is a condition in which imaging plays an important part. Imaging findings are included in classification criteria, often for diagnostic purposes. Inclusion of a magnetic resonance imaging (MRI) definition of sacroiliitis has made it possible to individualize/recognize nonradiographic axial SpA1. But conventional MRI has not resolved all the problems in SpA, such as the possibility of overdiagnosis and low performance as an outcome measure or therapeutic evaluation tool2. Under these circumstances, new imaging tools may represent an advance in disease assessment, particularly for locations that are difficult to access using conventional imaging or (sometimes) during clinical examination. This may be the case for enthesitis, the hallmark of spondyloarthritis; imaging provides objective proof of inflammatory involvement, and allows the differential diagnosis with other painful disorders of the entheses, such as fibromyalgia3,4. In this issue of The Journal , Althoff, et al 5 compared whole-body MRI (wbMRI) imaging versus clinical examination of enthesitis in patients with early axial SpA (disease duration < 5 yrs) during 3 years of continuous anti-tumor necrosis factor (TNF) therapy. This design is interesting because it may give an idea of the performance of clinical and wbMRI enthesitis assessment in early disease (with potential diagnostic and prognostic implications) and during longterm anti-TNF therapy … Address correspondence to Dr. D. Wendling, University Hospital J. Minjoz, Rheumatology, Boulevard Fleming, Besançon 25030, France; E-mail: dwendling{at}chu-besancon.fr
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 | 0.035 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".