Studying the accuracy of infrared thermography for measuring core body temperature
Bibliographic record
Abstract
The purpose of this study was to investigate the accuracy of infrared thermography for measuring body temperature. We compared a commercially available infrared thermal imaging camera (FLIR One) with a medical-grade oral thermometer (Welch-Allyn) as a gold standard. Measurements using the thermal imaging camera were taken from both a short distance (10cm) and long distance (50cm) from the subject. Thirty young healthy adults participated in a study that manipulated body temperature. After establishing a baseline, participants lowered their body temperature by placing their feet in a cold-water bath for 30 minutes while consuming cold water. Feet were then removed and covered with a blanket for 30 minutes as body temperature returned to baseline. During the course of the 70-minute experiment, body temperature was recorded at a 10-minute interval. The thermal imaging camera demonstrated a significant temperature difference from the gold standard from both close range (mean error: +0.433°C) and long range (mean error: +0.522°C). Despite demonstrating potential as a fast and non-invasive method for temperature screening, our results indicate that infrared thermography does not provide an accurate measurement of body temperature. As a result, infrared thermography is not recommended for use as a fever screening device.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".