Predicting Delayed Shock in Multisystem Inflammatory Disease in Children
Bibliographic record
Abstract
OBJECTIVES: Patients with multisystem inflammatory disease in children (MIS-C) are at risk of developing shock. Our objectives were to determine independent predictors associated with development of delayed shock (≥3 hours from emergency department [ED] arrival) in patients with MIS-C and to derive a model predicting those at low risk for delayed shock. METHODS: We conducted a retrospective cross-sectional study of 22 pediatric EDs in the New York City tri-state area. We included patients meeting World Health Organization criteria for MIS-C and presented April 1 to June 30, 2020. Our main outcomes were to determine the association between clinical and laboratory factors to the development of delayed shock and to derive a laboratory-based prediction model based on identified independent predictors. RESULTS: Of 248 children with MIS-C, 87 (35%) had shock and 58 (66%) had delayed shock. A C-reactive protein (CRP) level greater than 20 mg/dL (adjusted odds ratio [aOR], 5.3; 95% confidence interval [CI], 2.4-12.1), lymphocyte percent less than 11% (aOR, 3.8; 95% CI, 1.7-8.6), and platelet count less than 220,000/uL (aOR, 4.2; 95% CI, 1.8-9.8) were independently associated with delayed shock. A prediction model including a CRP level less than 6 mg/dL, lymphocyte percent more than 20%, and platelet count more than 260,000/uL, categorized patients with MIS-C at low risk of developing delayed shock (sensitivity 93% [95% CI, 66-100], specificity 38% [95% CI, 22-55]). CONCLUSIONS: Serum CRP, lymphocyte percent, and platelet count differentiated children at higher and lower risk for developing delayed shock. Use of these data can stratify the risk of progression to shock in patients with MIS-C, providing situational awareness and helping guide their level of care.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".