Infrared Skin Thermometry: Validating and Comparing Techniques to Detect Periwound Skin Infection
Bibliographic record
Abstract
OBJECTIVE: Diagnosis of wound infection can be challenging because it relies on a combination of clinical signs and symptoms that are often nonspecific. Increased periwound cutaneous temperature is a classic sign of deep and surrounding wound infection, and its quantitative measurement is one of the most reliable and valid clinical signs of deep and surrounding skin infection at the bedside. Skin surface temperature differences may be detected using commercially available noncontact infrared thermometers. However, techniques to detect temperature using noncontact infrared thermometers vary, and no studies have evaluated these methods. Two such measurement techniques include the "4-point" and "whole-wound" scanning methods. This study assessed the ability of different infrared thermometers using the aforementioned techniques to detect clinically meaningful differences in periwound cutaneous temperatures used in the diagnosis of wound infection. METHODS: A prospective cohort study was conducted from 2015 to 2016 of consenting adult patients 18 years or older with an open wound attending a regional wound care clinic. One hundred patients with wounds underwent surface temperature measurement. Infection was not a specific inclusion criterion as the primary objective was to conduct a comparative assessment of infrared thermometry devices. Demographic data (age, height, weight, gender, and ethnicity) were also collected. Each wound was measured using 4 different noncontact infrared thermometers: Exergen DermaTemp 1001 (Exergen Corporation, Watertown, Massachusetts), Mastercraft Digital Temperature Reader (Mastercraft, Toronto, Ontario, Canada), Mastercool MSC52224-A (Mastercool Inc, Randolph, New Jersey), and Etekcity ETC-8250 Temperature Heat Pen (Etekcity, Anaheim, California). Data analysis was based on a comparison of 4 periwound skin surface temperature measurement differences (ΔT in degrees Fahrenheit) between the wound site and an equivalent contralateral control site. OUTCOME MEASURES: The primary outcome of the ability of each thermometer to detect a clinically significant difference in temperature was assessed with χ analysis. Paired t tests were conducted to compare the ΔT measurements of each specific thermometry device between the 2 measurement techniques. Pearson product moment correlation coefficients were calculated for the temperature ΔT for both measuring techniques for all devices to determine level of agreement. A 1-way analysis of variance was conducted to compare temperature measurements among the infrared thermometry devices. MAIN RESULTS: There was no significant difference in the ability of each thermometer to detect a clinically meaningful difference of 3° F by either the 4-point (P = .10) or whole-wound techniques (P = .67). When a definition of 4° F was used, results were similar (4-point, P = .15; whole wound, P = .20). Comparisons among devices and techniques showed strong correlations (>0.80). Etekcity ETC-8250 and the 4-point measurement compared with the Exergen device using the whole-wound technique had a correlation coefficient of 0.72. Spearman ρ demonstrated a similarly high degree of correlation between techniques and devices, and only the Etekcity ETC-8250 device had a coefficient of 0.71 to 0.90 when compared with others. Paired t testing for each thermometry device comparing measurement techniques for raw temperatures ΔT demonstrated no significant difference (P > .05). No statistical differences among the ΔT values for the 3 different thermometers were noted when using the whole-wound method (P = .095). Similarly, no significant differences among the ΔT values were noted for the 4 different thermometers when using the 4-point method (P = .10). CONCLUSIONS: The results of this study demonstrate that both the 4-point and whole-wound methods of temperature acquisition using cost-efficient infrared thermometers provide accurate and similar results in clinical wound care settings.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".