Heart Rates in Hospitalized Children by Age and Body Temperature
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Heart rate (HR) is frequently used by clinicians in the hospital to assess a patient's severity of illness and make treatment decisions. We sought to develop percentiles that characterize the relationship of expected HR by age and body temperature in hospitalized children and to compare these percentiles with published references in both primary care and emergency department (ED) settings. METHODS: Vital sign data were extracted from electronic health records of inpatients <18 years of age at 2 large freestanding children's hospitals from July 2011 to June 2012. We selected up to 10 HR-temperature measurement pairs from each admission. Measurements from 60% of patients were used to derive the percentile curves, with the remainder used for validation. We compared our upper percentiles with published references in primary care and ED settings. RESULTS: We used 60,863 observations to derive the percentiles. Overall, an increase in body temperature of 1°C was associated with an increase of ∼ 10 beats per minute in HR, although there were variations across age and temperature ranges. For infants and young children, our upper percentiles were lower than in primary care and ED settings. For school-age children, our upper percentiles were higher. CONCLUSIONS: We characterized expected HR by age and body temperature in hospitalized children. These percentiles differed from references in primary care and ED settings. Additional research is needed to evaluate the performance of these percentiles for the identification of children who would benefit from further evaluation or intervention for tachycardia.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".