Implementation and adherence to the Bedside Paediatric Early Warning System (BedsidePEWS) in a pediatric tertiary care hospital
Bibliographic record
Abstract
Abstract BackgroundClinical deterioration in children admitted to hospital wards often manifests through signs of increasing illness severity that may lead to unplanned Pediatric Intensive Care Unit admissions or cardiac arrest, if undetected. The Bedside Pediatric Early Warning System (BedsidePEWS) is a validated Canadian scoring system used at a large tertiary care children’ hospital to prevent critical illness and standardize the response to deteriorating children on the wards.MethodsA 6-month audit was performed to evaluate the use of the BedsidePEWS, escalation of patient observations, monitoring and medical reviews on the wards in 2018.Two research nurses performed weekly visits to the hospital wards to collect data on BedsidePEWS scores, medical reviews, type of monitoring and vital signs recorded. Data were described through means or medians according to the distribution. Inferences were calculated either with Chi-square, Student’s t test or Wilcoxon-Mann–Whitney test, as appropriate (P <0.05 considered as significant).ResultsA total of 522 Vital Signs (VS) and score calculations on 177 patient clinical records were observed from 13 hospital inpatient wards. Frequency of VS and score documentation occurred <3 times per day in 33% of the observations. Adherence to the VS documentation frequency according to the hospital protocol was observed in 54% for all patients; for children with chronic health conditions (CHC) it was significantly lower than children admitted for acute medical conditions (47%, P=0.006). The BedsidePEWS score was correctly calculated and documented in 84% of the observed VS documentation events. Systolic blood Pressure was recorded in 79% and Temperature in 91% of the VS recording events. Patients within a 0-2 BedsidePEWS score range were all reviewed at least once a day by a physician. Only 50% of the patients in the 5-6 score range were reviewed within 4 hours and 42% of the patients with a score ≥7 within 2 hours. Transcutaneous Oxygen Saturation continuous monitoring was applied to 60% of the children at higher risk (BedsidePEWS ≥5).ConclusionsEscalation of patient observations, monitoring and medical reviews matching the BedsidePEWS is still suboptimal. Children with CHC are at higher risk of lower compliance. Impact of adherence to predefined response algorithms on patient outcomes should be further explored.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".