Laser Doppler Perfusion Imaging Is Useful in the Study of Raynaud’s Phenomenon and Improves the Capillaroscopic Diagnosis
Bibliographic record
Abstract
OBJECTIVE: To investigate capillary morphology and skin blood flow of dorsal hands by nailfold videocapillaroscopy (NVC) and laser Doppler perfusion imaging (LDPI), respectively, in patients with primary Raynaud's phenomenon (PRP) and systemic sclerosis (SSc) and to compare the results with those obtained in healthy controls. METHODS: The study group consisted of 142 patients with SSc, 88 patients with PRP, and 147 healthy controls. NVC was performed in all the groups examined. In patients with SSc the capillaroscopic pattern was classified as early, active, or late group pattern. A baseline skin blood flow determination of the dorsum of the subject's hands was acquired through a low-energy 670 nm Lisca Laser Doppler Perfusion Imager. RESULTS: In the healthy controls the perfusion distribution pattern was homogeneous, with a proximal-distal perfusion gradient. In patients with PRP, the perfusion distribution pattern was homogeneous, but the proximal-distal perfusion gradient was absent. Finally, in patients with SSc the perfusion distribution pattern was dyshomogeneous and a proximal-distal gradient was absent. The minimum perfusion, mean perfusion, maximum perfusion, and standard deviation, calculated as variation by means of each measurement site, were significantly different in all the groups examinated. CONCLUSION: NVC represents the best method to analyze microvascular damage in rheumatic diseases. LDPI improves the evaluation of vascular damage in patients with SSc. The LDPI and the capillaroscopic images fully matched the definition of the various stages of vascular digital damage in SSc.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".