Pediatric early warning score and deteriorating ward patients on high‐flow therapy
Bibliographic record
Abstract
BACKGROUND: Delivery of non-invasive ventilation commonly occurs in the pediatric intensive care unit (PICU). With the advent of high-flow nasal cannula (HFNC), patients with respiratory distress may be rescued on the ward without a PICU admission. We evaluated our ward HFNC algorithm to determine its safety profile and independent predictors for non-responders, defined as requiring subsequent PICU admission. METHODS: A retrospective chart review of patients <17 years of age admitted with respiratory distress between 2016 and 2017 was carried out. Pediatric Early Warning System (PEWS) respiratory score was used to assess the clinical response of patients requiring HFNC. Variables associated with non-responders were evaluated, and their PICU admission was studied for escalation of care and criticality. RESULTS: Patients with comorbidities (P = 0.02) were more likely to require HFNC. Of the 18 patients initiated on HFNC, 44% (n = 8) remained on the ward. Non-responders (n = 10; 56%) had higher (2.7 vs 1.8; P = 0.03) and worsening (-0.1 vs 0.3; P = 0.05) PEWS respiratory scores 90 min after HFNC initiation. Eighty percent (n = 8) of non-responders required escalation to continuous positive airway pressure or bilevel positive airway pressure in the PICU. For both HFNC responders and non-responders, there were no requirements for intubation, evidence of air leak or difference in days of respiratory support. CONCLUSIONS: High and worsening PEWS scores 90 min after HFNC initiation may indicate non-response when coupled with a standardized ward HFNC algorithm for respiratory distress. Further improvements may be seen with an earlier initiation of HFNC in the emergency department and more aggressive flow escalation on the ward.
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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 | 0.000 | 0.003 |
| 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 | 0.000 | 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".