Development and Prospective Validation of Tools to Accurately Identify Neurosurgical and Critical Care Events in Children With Traumatic Brain Injury*
Bibliographic record
Abstract
OBJECTIVE: To develop and validate case definitions (computable phenotypes) to accurately identify neurosurgical and critical care events in children with traumatic brain injury. DESIGN: Prospective observational cohort study, May 2013 to September 2015. SETTING: Two large U.S. children's hospitals with level 1 Pediatric Trauma Centers. PATIENTS: One hundred seventy-four children less than 18 years old admitted to an ICU after traumatic brain injury. MEASUREMENTS AND MAIN RESULTS: Prospective data were linked to database codes for each patient. The outcomes were prospectively identified acute traumatic brain injury, intracranial pressure monitor placement, craniotomy or craniectomy, vascular catheter placement, invasive mechanical ventilation, and new gastrostomy tube or tracheostomy placement. Candidate predictors were database codes present in administrative, billing, or trauma registry data. For each clinical event, we developed and validated penalized regression and Boolean classifiers (models to identify clinical events that take database codes as predictors). We externally validated the best model for each clinical event. The primary model performance measure was accuracy, the percent of test patients correctly classified. The cohort included 174 children who required ICU admission after traumatic brain injury. Simple Boolean classifiers were greater than or equal to 94% accurate for seven of nine clinical diagnoses and events. For central venous catheter placement, no classifier achieved 90% accuracy. Classifier accuracy was dependent on available data fields. Five of nine classifiers were acceptably accurate using only administrative data but three required trauma registry fields and two required billing data. CONCLUSIONS: In children with traumatic brain injury, computable phenotypes based on simple Boolean classifiers were highly accurate for most neurosurgical and critical care diagnoses and events. The computable phenotypes we developed and validated can be used in any observational study of children with traumatic brain injury and can reasonably be applied in studies of these interventions in other patient populations.
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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 | 0.024 | 0.114 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".