Wrist and Forehead Temperature Measurement as Screening Methods During the COVID-19 Pandemic
Bibliographic record
Abstract
Background: Temperature screening checkpoints have become widely distributed during the COVID-19 pandemic, using various contactless methods of temperature measurement, including wrist and forehead measurement. Aim: In this study we aim to investigate the sensitivity and specificity of these two temperature measurement methods – wrist and forehead – compared with the standards of sublingual or axillary measurement. We also aim to investigate the influence of age, gender, device brand and diurnal effect on the temperature reading. Methods: Participants were randomly assigned to one of two groups, each group using a different temperature measurement device. All participants had their forehead and wrist temperature measured, and this was compared to their axillary or sublingual readings. Results: The area under the curve for wrist measurement was 0.49 (95% CI 0.34 and 0.64), p>0.05, with a sensitivity of 46.2% and specificity of 53.3%, while the area under the curve for forehead measurement was 0.70 (95% CI 0.51, 0.89), p<0.05, with a sensitivity of 23.1% and specificity of 76.9%, PPV 1.59% and NPV 97.7%. Conclusion: Wrist and forehead temperature measurement is not accurate in detecting fever during the ongoing COVID-19 pandemic. Although forehead measurement is also not an ideal method, it nevertheless appears more consistent than wrist measurement.
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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 | 0.028 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 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".