Raynaud Phenomenon in Systemic Sclerosis: Does Digital Thermal Monitoring Correlate to Specific Nailfold Videocapillaroscopy Abnormalities?
Bibliographic record
Abstract
Objective. Early diagnosis of systemic sclerosis (SSc) is imperative, and Raynaud phenomenon (RP) is an important component of progressive vasculopathy. Nailfold videocapillaroscopy (NVC) is a well-established tool that can quantify structural vascular abnormalities. Digital thermal monitoring (DTM) assesses microvascular functional dysfunction related to thermoregulation. In this study, we investigated the correlation of NVC patterns and DTM variables in patients with SSc. Methods. Patients with SSc according to the 2013 American College of Rheumatology/European League Against Rheumatism criteria who consented and enrolled in the clinical care registry had NVC and DTM performed. For NVC, the number of capillaries (density), measurement of apical diameter (dimension), presence or absence of hemorrhages, and number of abnormal shapes were assessed to categorize 3 different qualitative patterns: early, active, and late. For DTM, Doppler ultrasound hyperemic, low frequency, blood velocity of radial artery, and fingertip vascular function were assessed, and a vascular reactivity index (VRI) measurement was automated. Statistical evaluation was performed by nonparametric tests to assess the correlation of NVC and VRI. Results. Thirty-one SSc subjects with interpretable NVC and DTM performed on the same day were included in the study. VRI was progressively higher in SSc patients with early, active, and late NVC patterns of microangiopathy (P < 0.0001). There was a significant negative correlation between VRI and microhemorrhages scores (r = –0.363, P = 0.044). Conclusion. Our study suggests that more advanced vasculopathy correlates to reduced microvascular function as detected by DTM and more advanced structural abnormalities detected by NVC. NVC and DTM may provide different aspects of vasculopathy quantification and complement each other as investigative tools.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".