Use of Laser Speckle Contrast Imaging to Assess Digital Microvascular Function in Primary Raynaud Phenomenon and Systemic Sclerosis: A Comparison Using the Raynaud Condition Score Diary
Bibliographic record
Abstract
OBJECTIVE: Evaluate objective assessment of digital microvascular function using laser speckle contrast imaging (LSCI) in a cross-sectional study of patients with primary Raynaud phenomenon (RP) and systemic sclerosis (SSc), comparing LSCI with both infrared thermography (IRT) and subjective assessment using the Raynaud Condition Score (RCS) diary. METHODS: Patients with SSc (n = 25) and primary RP (n = 18) underwent simultaneous assessment of digital perfusion using LSCI and IRT with a cold challenge on 2 occasions, 2 weeks apart. The RCS diary was completed between assessments. The relationship between objective and subjective assessments of RP was evaluated. Reproducibility of LSCI/IRT was assessed, along with differences between primary RP and SSc, and the effect of sex. RESULTS: There was moderate-to-good correlation between LSCI and IRT (Spearman rho 0.58-0.84, p < 0.01), but poor correlation between objective assessments and the RCS diary (p > 0.05 for all analyses). Reproducibility of IRT and LSCI was moderate at baseline (ICC 0.51-0.63) and immediately following cold challenge (ICC 0.56-0.86), but lower during reperfusion (ICC 0.3-0.7). Neither subjective nor objective assessments differentiated between primary RP and SSc. Men reported lower median daily frequency of RP attacks (0.82 vs 1.93, p = 0.03). Perfusion using LSCI/IRT was higher in men for the majority of assessments. CONCLUSION: Objective and subjective methods provide differing information on microvascular function in RP. There is good convergent validity of LSCI with IRT and acceptable reproducibility of both modalities. Neither subjective nor objective assessments could differentiate between primary RP and SSc. Influence of sex on subjective and objective assessment of RP warrants further evaluation.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".