Skin Perfusion of Fingers Shows a Negative Correlation with Capillaroscopic Damage in Patients with Systemic Sclerosis
Bibliographic record
Abstract
To the Editor: Two variants of laser Doppler monitoring exist to evaluate digital blood flow: laser Doppler imaging (LDI) and laser Doppler flowmetry (LDF). The first technique uses a scanning method with a distant light source and detector, while the second uses optical fibers to carry the light to and from the tissue. As a result, LDF measures the microcirculatory blood flow through a very small volume of tissue, whereas LDI scans a larger tissue area. LDF continuously measures skin blood perfusion; because of the scanning time, LDI cannot measure it continuously1. We address the relationship between digital blood flow and capillaroscopic damage in patients with systemic sclerosis (SSc). Table 1 outlines the main studies. View this table: Table 1. Main studies of laser Doppler monitoring and capillaroscopy to evaluate microvascular damage in patients with systemic sclerosis (SSc). Using LDI, Correa, et al found lower digital blood flow in 44 patients with SSc compared with healthy controls at baseline and after cold stimulus2. No correlation was found between functional … Address correspondence to Prof. Salsano; E-mail: felice.salsano{at}uniroma1.it
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".