Development and Validation of a Model to Predict Pediatric Septic Shock Using Data Known 2 Hours After Hospital Arrival
Bibliographic record
Abstract
Objectives: To use electronic health record data from the first 2 hours of care to derive and validate a model to predict hypotensive septic shock in children with infection. Design: Derivation-validation study using an existing registry. Setting: Six emergency care sites within a regional pediatric healthcare system. Three datasets of unique visits were designated: Patients: Patients in whom clinicians were concerned about serious infection from 60 days to 18 years were included; those with septic shock in the first 2 hours were excluded. There were 2,318 included visits; 197 developed septic shock (8.5%). Interventions: Lasso with 10-fold cross-validation was used for variable selection; logistic regression was then used to construct a model from those variables in the training set. Variables were derived from electronic health record data known in the first 2 hours, including vital signs, medical history, demographics, and laboratory information. Test characteristics at two thresholds were evaluated: 1) optimizing sensitivity and specificity and 2) set to 90% sensitivity. Measurements and Main Results: Septic shock was defined as systolic hypotension and vasoactive use or greater than or equal to 30 mL/kg isotonic crystalloid administration in the first 24 hours. A model was created using 20 predictors, with an area under the receiver operating curve in the training set of 0.85 (0.82–0.88); 0.83 (0.78–0.89) in the temporal test set and 0.83 (0.60–1.00) in the geographic test set. Sensitivity and specificity varied based on cutpoint; when sensitivity in the training set was set to 90% (83–94%), specificity was 62% (60–65%). Conclusions: This model predicted risk of septic shock in children with suspected infection 2 hours after arrival, a critical timepoint for emergent treatment and transfer decisions. Varied cutpoints could be used to customize sensitivity to clinical context.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".