PRAM Score as Predictor of Pediatric Asthma Hospitalization
Bibliographic record
Abstract
OBJECTIVES: The objective was to determine the association between asthma severity as measured by the Pediatric Respiratory Assessment Measure (PRAM) score and the likelihood of admission for pediatric patients who present to the emergency department (ED) with moderate-to-severe asthma exacerbations and who receive intensive asthma therapy. METHODS: This was a secondary analysis of a prospective study of triage nurse-initiated steroid therapy in pediatric asthma. Children aged 2 to 17 years inclusive, presenting with moderate-to-severe acute asthma exacerbations (defined as PRAM ≥ 4), were included. To be eligible for inclusion in the study, children must have received "intensive asthma therapy," defined as nurse-initiated initial bronchodilator and oral steroid therapy at arrival to triage. PRAM scores were measured hourly as per ED protocol. The primary outcome was inpatient hospitalization; secondary outcome was ED stay greater than 8 hours. Logistic regression models were used to predict admission based on PRAM score at triage and then hourly thereafter. The area under the receiver operating characteristic curve (AUC) was calculated for each hour. RESULTS: A total of 297 patients were included in the analysis, with an admission rate of 11.4% for patients receiving intensive therapy. The 3-hour PRAM (AUC = 0.85) significantly improved prediction of admission compared to PRAM at triage (p = 0.04). CONCLUSIONS: The 3-hour PRAM scores best predicts the need for hospitalization. These results may be applied in clinical settings to facilitate the decision to admit or initiate more aggressive adjunctive therapy to decrease the need for hospitalization.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".