A Descriptive Study of Skin Temperature, Tissue Perfusion, and Tissue Oxygen in Patients With Chronic Venous Disease
Bibliographic record
Abstract
Chronic inflammation and microcirculatory disturbances of the skin have been implicated as causative factors of complications associated with chronic venous disease (CVD). The purpose of this study is to describe the mean differences between and correlations among three measures of microcirculation: skin temperature (Tsk), tissue perfusion/blood flow (BF), and tissue oxygen (tcPO(2)) of CVD-inflamed skin compared to normal controls. In a convenience sample of 55 patients with CVD (n = 31) and without CVD (n = 24), Tsk was measured with an infrared thermometer, BF with a laser Doppler flowmeter, and tcPO( 2) with a transcutaneous oximeter across three measurements periods 1 week apart (Times 1, 2, and 3) at the medial aspect of both lower legs. Tsk was higher (1.2 degrees C) across all measurement periods (p < .05), BF was higher at Times 1 and 3 (p = .002 and .012, respectively), and tcPO(2) was lower at Times 1 and 3 (p = .013 and .050, respectively) in the CVD group as compared to the non-CVD group. BF and Tsk were positively correlated at Times 1 and 2 (r = .516, p < .005; r = 0.278, p = .04) but not at Time 3 (r = 0.235, p > .05). No consistently significant correlations were found between tcPO(2) and BF or tcPO(2) and Tsk (p > .05). Tsk and BF were higher in the skin of lower legs affected by CVD than in those not affected. Pathological processes in the skin produce heat detectable by an infrared thermometer. Measurement and monitoring of Tsk can augment clinical findings and guide treatment when localized inflammation is suspected. Future studies of Tsk should be directed toward the usefulness of infrared technology to develop a CVD leg ulcer prediction model.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".