BIOMEDICAL APPLICATION OF THERMOCHROMIC LIQUID CRYSTALS AND LEUCO DYES FOR TEMPERATURE MONITORING IN THE EXTREMITIES
Bibliographic record
Abstract
INTRODUCTION:The researcher developed a prototype of a novel thermochromic liquid crystal (TLC)-coated fabric with an extended temperature range and enhanced sensitivity.Employing both color and pattern recognition into the fabric, rapid determination of the underlying pedal temperature is facilitated.PURPOSE: The purpose of this study was to evaluate the accuracy of the fabric as a potential diagnostic aid for identifying complications in the high-risk foot.METHODS: The hands of one hundred subjects were used to compare the average maximum temperatures indicated by the fabric versus standard thermal camera images.Findings were statistically analyzed using a paired t-test with significance defined as p<0.05.RESULTS: With the exception of the tip of the thumb and regions in the palm, there were no significant differences between average maximum temperatures measured with the thermal camera and those detected with the TLC fabric.CONCLUSION: Using direct visual analysis, the researcher demonstrated that a novel TLC fabric was able to accurately map temperatures in the palmar surface of the hand.The findings support the continued development of a temperature-sensitive sock that can be used in home to monitor for temperature changes that may indicate the onset of high-risk foot complications.I would like to acknowledge the Kent State University team members who have worked with me on the development of the initial prototype of the fabric used in the first study, including John West, PhD, from the Liquid Crystal Institute, Yijing
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".