Provisional Validation of a Pediatric Early Warning Score for Resource-Limited Settings
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The use of Pediatric Early Warning Scores is becoming widespread to identify and rapidly respond to patients with deteriorating conditions. The ability of Pediatric Early Warning Scores to identify children at high risk of deterioration or death has not, however, been established in resource-limited settings. METHODS: We developed the Pediatric Early Warning Score for Resource-Limited Settings (PEWS-RL) on the basis of expert opinion and existing scores. The PEWS-RL was derived from 6 equally weighted variables, producing a cumulative score of 0 to 6. We then conducted a case-control study of admissions to the pediatrics department of the main public referral hospital in Kigali, Rwanda between November 2016 and March 2017. We defined case patients as children fulfilling the criteria for clinical deterioration, who were then matched with controls of the same age and hospital ward. RESULTS: During the study period, 627 children were admitted, from whom we selected 79 case patients and 79 controls. For a PEWS-RL of ≥3, sensitivity was 96.2%, and specificity was 87.3% for identifying patients at risk for clinical deterioration. A total PEWS-RL of ≥3 was associated with a substantially increased risk of clinical deterioration (odds ratio 129.3; 95% confidence interval 38.8–431.6; P <.005). CONCLUSIONS: This study reveals that the PEWS-RL, a simple score based on vital signs, mental status, and presence of respiratory distress, was feasible to implement in a resource-limited setting and was able to identify children at risk for clinical deterioration.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".