A Cross-Sectional Study on Subjective Fever Assessment in Children by Palpation: Are Fathers as Reliable as Mothers?
Bibliographic record
Abstract
BACKGROUND: Fever is common in pediatric patients. Often, parents rely solely on palpation when assessing their child's fever. The objective of the current study was to determine the accuracy of parents in detecting their child's fever by palpation. METHODS: A prospective cross-sectional study was conducted at the emergency department (ED) of a tertiary pediatric hospital. Infants and children, 0-4 years of age, presenting to the ED with both parents were included. Parents were separately asked if their child had a fever and, if so, were asked to assess the temperature by palpation. A nurse obtained the rectal temperature. The primary outcome measure was the accuracy of fathers and mothers in detecting fever. RESULTS: A total of 170 children with their parents were enrolled. The mean ages of the children, mothers, and fathers were 18.9 (SD 0.8) months, 31.1 (SD 6.4) years, and 33.7 (SD 6.9) years, respectively. No statistically significant difference was found between mothers and fathers in the ability to assess fever by palpation (OR 0.65, 95% CI 0.39,-1.08). Sensitivities for detecting fever by palpation for mothers and father were 86.4% and 88.2%, respectively (specificity among mothers: 54.2% and specificity among fathers: 43.1%). The overall negative and positive predictive values were 65.9% (95% CI 55%-75.7%) and 75.7% (95% CI 69.9%-80.8%), respectively. CONCLUSIONS: Mothers and fathers do not differ in their ability to accurately assess their child's fever by palpation. The low positive and negative predictive values indicate that if temperature was not measured, physicians cannot rely on parents' reports.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".