Investigating the Risk Factors Associated With Acute Neurologic Dysfunction in Pediatric Hyperglycemic Emergencies on Transport
Bibliographic record
Abstract
OBJECTIVE: This study aims to identify key characteristics of hyperglycemic emergencies in pediatric patients and those at risk for acute neurologic dysfunction during transport. METHODS: We conducted a retrospective chart review of pediatric patients during interfacility transport by Ornge, Ontario's critical care transport service, from January 1, 2009, to December 31, 2019. Data were extracted from electronic patient care records and included demographic, clinical, and transport-specific variables. Two multiple logistic regression models were utilized to analyze associations between predictor variables and neurologic dysfunction (GCS, <14). RESULTS: Of the 399 patients included, 24% (n = 95) had a GCS score of <14. Patients with a GCS score of <14 were more acidotic compared with those with a GCS score of ≥14 (median pH, 6.9 [IQR, 6.8-7.1] vs median, pH 7.0 [IQR, 1.0-7.2]; P < 0.001). Higher median corrected sodium for glucose values were observed in patients with a GCS score of <14 compared to those with a GCS score of ≥14 (145.7 mmol/L [IQR, 140.6-149.9 mmol/L] vs 141.7 mmol/L [IQR, 138.3-146.4 mmol/L]; P < 0.001). Multiple logistic regression identified younger age (aOR, 0.91; 95% CI, 0.84-0.98; P = 0.01), severe acidosis (pH <7.10; aOR, 3.56; 95% CI, 1.33-11.62; P = 0.02), and higher creatinine (aOR, 1.01; 95% CI, 1.01-1.02; P < 0.001) as risk factors for acute neurologic dysfunction. CONCLUSIONS: Our findings reveal associations between acute neurologic dysfunction, younger age, severe acidosis, and elevated corrected sodium for glucose values in pediatric hyperglycemic emergencies during transport. Education and adherence to guidelines are recommended to improve outcomes in this population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".