Erosive progression by high-resolution peripheral quantitative computed tomography and conventional radiography in rheumatoid arthritis
Bibliographic record
Abstract
OBJECTIVES: To investigate the diagnostic accuracy of high-resolution peripheral quantitative computed tomography (HR-pQCT) to assess erosive progression during 1 year compared with conventional radiography (CR) in rheumatoid arthritis (RA). METHODS: This prospective study included 359 patients with RA (disease duration ≥5 years) between March 2018 and October 2020. HR-pQCT and CR were obtained at inclusion and after 1 year. Erosive assessment was performed at two metacarpophalangeal joints of the dominant hand using HR-pQCT, and progression was defined as an increase in erosion number ≥1 or an increase in erosive volume > least significant change. CR of hands, wrists and feet was evaluated using Sharp/van der Heijde scores and erosive progression was defined as a 1.1-point increase in erosion score according to the smallest detectable change. RESULTS: In paired analyses (n = 310), erosive progression was identified in 30 patients using CR and in 40 patients using HR-pQCT. In the 40 patients with erosive progression on HR-pQCT, progression was not identified by CR in 33 patients. Adding HR-pQCT to CR doubled the proportion of patients identified with progression from 30 (10%) to 63 (20%) patients. Using CR as the reference, the sensitivity of HR-pQCT for identifying erosive progression was 23.3% (95% CI: 9.9, 42.3%) and the specificity was 88.2% (95% CI: 83.8, 91.7%). CONCLUSION: A substantial proportion of patients with erosive progression are overlooked using CR only to monitor erosive progression. Adding high-resolution peripheral CT to CR doubles the proportion of patients who may benefit from individualized therapy targeting erosive progression in RA.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".