The Accuracy of Caregiver’s ‘hot to touch’ assessment in paediatric healthcare among children aged 6-35 months with medically-attended diarrhea: Findings from the EFGH- <i>Shigella</i> surveillance in Kenya, Malawi, Bangladesh and Peru, 2022-2024
Bibliographic record
Abstract
Abstract Introduction Confidence in caregivers’ assessment of fever in their children, compared to thermometer readings, could help guide prompt care seeking and appropriate treatment in settings where access to reliable diagnostic tools is limited. Here, we evaluated the accuracy and drivers of caregiver-reported ‘hot-to-touch’ fever compared to digital thermometry among children in the Enterics for Global Health (EFGH) Shigella surveillance study. Methods Children aged 6–35 months with medically attended diarrhea (MAD) enrolled in Kenya, Malawi, Bangladesh, and Peru between August 2022 and August 2024 were included. We trained caregivers to assess and record daily ‘hot-to-touch’ (subjective fever measurement) and digital (thermometer) axillary temperature (fever defined as ≥37.5°C) readings over for 14 days post-enrolment. We calculated site specific and overall accuracy of ‘hot-to-touch’ compared to thermometer-measured fever and used multivariable Poisson regression to identify factors associated with accurate detection. Results The accuracy of caregiver-reported ‘hot-to-touch’ assessment ranged from 62.1% to72.0% overall and was highest in Bangladesh (83.2%–96.1%) and lowest in Malawi (47.4%–53.4%) over the 14 day assessment period. Accuracy was higher in children with chest indrawing (aPR=1.29, 95% CI: 1.04–1.60) and low respiratory rate (aPR=1.20, 95% CI: 1.11–1.29) and in children from wealthier households (Quintile 5: aPR=1.21, 95% CI: 1.01-1.44). Accuracy was lower among caregivers from households with ≥3 children (aPR=0.88, 95% CI: 0.79–0.99) and for children with low heart rate (aPR=0.76, 95% CI: 0.61–0.94). Conclusion Suboptimal accuracy of hot-to-touch compared to digital thermometers in detecting fever in this study supports the need for digital thermometer use and context-specific strategies to enhance early detection of fever, particularly in communities living in resource-poor settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".