Two-Dimensional near Infrared Spectroscopic Imaging of the Hand to Assess Microvascular Abnormalities in Systemic Sclerosis: A Pilot Study
Why this work is in the frame
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Bibliographic record
Abstract
Patients affected by systemic sclerosis (SSc) develop functional and structural microvascular alterations and progressive fibrosis of the skin and internal organs. Evaluation of skin microcirculation is an important clinical step in the workup of SSc patients. Near infrared (NIR) spectroscopy is a well-established non-invasive technique to assess haemoglobin oxygen saturation (StO 2 ) in the illuminated tissue. The recent development of NIR spectroscopic two-dimensional (2D) imaging offers the possibility of visualising StO 2 distribution in large tissue areas. This is particularly important in SSc characterised by a very heterogeneous spatial distribution of the microvascular abnormalities. In addition, the short acquisition time of NIR spectroscopic images allows microvascular “dynamic” conditions, such as the vascular response to physical or pharmacological stimuli, to be evaluated. The present study reports the results of the test application of NIR spectroscopic 2D imaging of the palmar whole-hand surface for the evaluation of peripheral microcirculatory dysfunction in one patient with SSc, as compared with a healthy control, both in “static” (resting) and in “dynamic” (ischaemia-reperfusion) conditions. Spatial heterogeneity of microvascular alterations associated with temporal heterogeneity in vascular reactivity to ischaemic challenge make 2D NIR spectroscopic imaging a promising tool in the assessment of SSc, as compared with the current available techniques. A NIR spectroscopic camera by Kent Imaging Inc, Calgary, Canada was used.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it