Development and Validation of Early Warning Criteria to Identify Escalated Care Events in Neonatal Intensive Care Unit Patients
Bibliographic record
Abstract
OBJECTIVE: This study aimed to identify and validate the diagnostic utility of a set of clinical and laboratory criteria (early warning criteria [EWC]) that portend a clinical deterioration event (escalated care event [ECE]) in neonatal intensive care unit (NICU) patients. STUDY DESIGN: Using the RAND appropriateness method, we first established a consensus on seven ECE, that is, events that require additional monitoring, treatment, or stay in the NICU or that were associated with morbidity. We then established consensus on EWC that could portend an ECE from an initial set of 32 potential EWC items to a final set of 10 items. The occurrence and nonoccurrence of EWC and ECE were prospectively identified and tracked over 9 weeks. RESULTS: Among 170 NICU patients studied (2,502 patient-days), the frequency of an EWC was 53 per 1,000 patient-days. Of these patients, 41% had an EWC and 16% had an ECE. An EWC was followed by an ECE within 72 hours, 37% of the time, and within a median time interval of 113 minutes. The sensitivity, specificity, positive predictive values, and negative predictive values of EWC in identifying an ECE were 0.96, 0.69, 0.37, and 0.99, respectively. CONCLUSION: A simple bedside NICU-specific EWC identifies neonates likely to develop ECEs in the NICU.
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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 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".