Comparison of Photo Optical Imaging with Musculoskeletal Ultrasound and Clinical Examination in the Assessment of Inflammatory Activity in Proximal Interphalangeal Joints in Rheumatoid Arthritis and Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: Lightscan is a novel, rapid, low-cost, easily operated and noninvasive imaging technology used to assess inflammatory activity in proximal interphalangeal (PIP) joints. The results are calculated automatically. To our knowledge, this is the first comparative study of photo optical imaging (POI), with clinical examination (CE), disease activity score at 28 joints (DAS28)-erythrocyte sedimentation rate (ESR), and musculoskeletal ultrasonography (US) in healthy subjects and patients with rheumatoid arthritis (RA) or osteoarthritis (OA). METHODS: There were 688 PIP joints of both hands examined in 87 subjects (38 RA, 21 OA, 28 healthy) by Lightscan and compared with CE for clinically swollen and tender joints, DAS28-ESR (only RA), and US. RESULTS: With US as reference, POI had a sensitivity of 74% and a specificity of 93%. In the receiver-operating curve (ROC) analysis, the Lightscan showed a higher sensitivity and specificity [area under the curve (AUC) 0.879] for the distinction of healthy subjects versus patients (OA, RA) than US in greyscale (GSUS; AUC 0.797) and power Doppler (PDUS; AUC 0.67). POI correlated significantly with GSUS (r 0.473, p < 0.01) and PDUS (r 0.486, p < 0.01). The agreement rates between POI and GSUS were up to 79%, between POI and PDUS up to 92%, and between POI and CE up to 66%. POI did not correlate with DAS28-ESR. CONCLUSION: The Lightscan is a new technology offering sensitive imaging detection of inflammatory changes in subjects with RA and OA with PIP arthritis. POI was more sensitive than CE and correlated significantly to GSUS and PDUS, while presenting a higher sensitivity and specificity for the detection of healthy subjects versus patients (RA, OA) based on the ROC analysis.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".