Comparison of fluorescence optical imaging, ultrasonography and clinical examination with magnetic resonance imaging as a reference in active rheumatoid arthritis patients
Bibliographic record
Abstract
BACKGROUND: Fluorescence optical imaging with indocyanine-green enhancement (FOI) is a new imaging modality for the assessment of hand arthritis. The objective of this study was to compare performance profiles of clinical examination (CE), US and FOI using MRI as a reference in the same active rheumatoid arthritis (RA) patients. METHODS: CE, US, FOI and MRI were performed on six subjects with active RA. Each sequence of FOI was divided into three phases based on indocyanine-green dynamics and the joints were graded semi-quantitatively. Sensitivities and specificities of CE, US and FOI were calculated using the RAMRIS synovitis score >0 as a reference in a total of 30 joints (the second to fifth metacarpophalangeal (MCP) joints and the wrist of the clinically dominant hand). RESULTS: FOI showed sensitivities and specificities, respectively, of 85% and of 94% for Phase-1 and 69% and 94% for Phase-2. Sensitivities and specificities were 100% and 35% for CE (tender or swollen), 92% and 41% for gray scale US, and 77% and 100% for color-Doppler US. CONCLUSIONS: The performance characteristics of FOI in detection of synovitis in patients with active RA are comparable to those of US and more specific than CE. FOI has a potential as an assessment modality of 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".