Three-dimensional Volumetric Ultrasound: A Valid Method for Blinded Assessment of Response to Therapy in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To assess the responsiveness and repeatability of volumetric power Doppler ultrasound (PDUS) evaluation of synovitis and bone erosions in rheumatoid arthritis (RA). METHODS: Twenty-three patients with RA (19 women, mean age 52.7 ± 12.6 yrs, mean disease duration 10.1 ± 8.6 yrs) were prospectively enrolled. All patients were beginning therapy with rituximab because of disease activity despite therapy with synthetic disease-modifying antirheumatic drugs and tumor necrosis factor-blocking agents. Patients underwent clinical, laboratory, and volumetric PDUS examination at baseline, 6 months, and 12 months. Ten centers participated in the study. Four centers recruited the patients and performed the volumetric acquisitions of PDUS images, while the remaining 6 centers assessed the PDUS volumes, blinded to the identity of patients and date of the visits. The most symptomatic hand and foot were scored for B-mode synovitis, synovial PD signal, and bone erosions. The repeatability of the volumetric PDUS assessment was investigated. RESULTS: An overall improvement in clinical and PDUS measurements was found at the followup assessments. The mean indexes for synovial PD signal and bone erosions and the number of sites with abnormalities decreased significantly throughout the followup (p < 0.05). The intraacquisition, intrareader reliability was excellent for all PDUS measurements (intraclass correlation coefficients > 0.9). CONCLUSION: The results of our pilot study suggest that volumetric PDUS can be responsive and repeatable in multicenter cohort studies of RA. This technique may minimize assessment biases and reduce acquisition variability in open-label and observational studies.
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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 | 0.018 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".