Clinical Bedside Tools to Assess Systemic Sclerosis Vasculopathy: Can Digital Thermal Monitoring and Sublingual Microscopy Identify Patients With Digital Ulcers?
Bibliographic record
Abstract
Objective. Sublingual microscopy assesses systemic sclerosis (SSc) vasculopathy. Digital thermal monitoring (DTM) may identify patients at risk for digital ulcer (DU). The purpose of this analysis was to assess sublingual microscopy and DTM in SSc patients with and without previous DU in order to determine the utility of these clinical tools. Methods. SSc registry patients with clinical data who had both DTM and sublingual microscopy on the same day were included in this cross-sectional analysis. DTM quantifies vascular reactivity index (VRI). Sublingual microscopy measures longitudinal red blood cell fraction (RBCfract) and perfused boundary region (PBR). We evaluated the pairwise association between VRI, RBCfract, and PBR in a monotonic relationship using Spearman rank correlation in the DU subset. Correlation coefficients (rs) and their 95% CIs were reported. Results. Ninety patients were included; 29 had digital pits and/or active DU and 61 never had a DU. The only significant clinical feature associated with DU was modified Rodnan skin score (P = 0.003) with DU being higher. The VRI was lower in patients with DU (P = 0.01). The higher the RBCfract, the lower PBR (rs = –0.71, 95% CI –0.86 to –0.47, P < 0.001). VRI was not associated with RBCfract or PBR (P = 0.24 or 0.55, respectively) in the patients with DU. Conclusion. DTM is a useful tool for assessing SSc-DU. While sublingual microscopy measurements did not significantly correlate to VRI in patients with SSc-DU, a longitudinal study may be more helpful in capturing vasculopathy activity prior to possible irreversible damage.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".