Pediatric Vital Sign Distribution Derived From a Multi-Centered Emergency Department Database
Bibliographic record
Abstract
Background: We hypothesized that current vital sign thresholds used in pediatric emergency department screening tools do not reflect observed vital signs in this population. We analyzed a large multi-centered database to develop heart rate and respiratory rate centile rankings and z-scores that could be incorporated into electronic health record emergency department screening tools and we compared our derived centiles to previously published centiles and Pediatric Advanced Life Support vital sign thresholds. Methods: Initial heart rate and respiratory rate data entered into the Cerner™ electronic health record at 169 participating hospitals’ emergency departments over five years (2009 through 2013) as part of routine care were analyzed. Analysis was restricted to non-admitted children (0 to <18 years). Centile curves and z-scores were developed using Generalized Additive Models for Location, Scale, and Shape. A split-sample validation using two-thirds of the sample was compared with the remaining one-third. Centile values were compared with results from previous studies and guidelines. Results: HR and RR centiles and z-scores were determined from ~1.2 million records. Empirically-derived 95th centiles for heart rate and respiratory rate were higher than previously published results and both deviated from Pediatric Advanced Life Support guideline recommendations. Conclusions: Heart and respiratory rate centiles derived from a large real-world non-hospitalized emergency department pediatric population can inform the modification of electronic and paper-based screening tools to stratify children by the degree of deviation from normal for age rather than dichotomizing children into groups having “normal” versus “abnormal” vital signs. Furthermore, these centiles also may be useful in paper-based screening tools and bedside alarm limits for children in areas other than the ED and may establish improved alarm limits for bedside monitors.
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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 | 0.004 | 0.023 |
| 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 | 0.001 | 0.001 |
| 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".