Distributions and Behavior of Vital Signs in Critically Ill Children by Admission Diagnosis*
Bibliographic record
Abstract
OBJECTIVES: Define the distributions of heart rate and intraarterial blood pressure in children at admission to an ICU based on admission diagnosis and examine trends in these physiologic signs over 72 hours from admission (or to discharge if earlier). DESIGN: A retrospective analysis of continuously acquired signals. SETTING: A quaternary and primary referral children's hospital with a general PICU and cardiac critical care unit. PATIENTS: One thousand two hundred eighty-nine patients less than 18 years old were analyzed. Data from individual patient admissions were divided into 19 groups by primary admission diagnosis or surgical procedure. INTERVENTIONS: None. MEASUREMENT AND MAIN RESULTS: Distributions at admission are dependent on patient age and admission diagnosis (p < 10(-6)). Heart rate decreases over time, whereas arterial blood pressure is relatively stable, with differences seen in the directions and magnitude of these trends when analyzed by diagnosis group (p < 10(-6)). Multiple linear regression analysis shows that patient age, diagnosis group, and physiologic vital sign value at admission explain 50-63% of the variation observed for that physiologic signal at 72 hours (or at discharge if earlier) with admission value having the greatest influence. Furthermore, the variance of either heart rate or arterial blood pressure for the individual patient is smaller than the variance measured at the level of the group of patients with the same diagnosis. CONCLUSIONS: This is the first study reporting distributions of continuously measured physiologic variables and trends in their behavior according to admission diagnosis in critically ill children. Differences detected between and within diagnostic groups may aid in earlier recognition of outliers as well as allowing refinement of patient monitoring strategies.
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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 | 0.001 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".