Description and Contribution of Brain Magnetic Resonance Imaging in Nontraumatic Critically Ill Children
Bibliographic record
Abstract
BACKGROUND: The authors aimed to collect all brain magnetic resonance imaging (MRI) performed in critically ill children in the authors' medical pediatric intensive care unit over a 2-year period (2012-2013) to (1) describe the findings and (2) assess its contribution on practical patient care. METHODS: This is a single-center and retrospective study. All children without traumatic brain injury who underwent a brain MRI during pediatric intensive care unit stays were included. To assess the exam's contribution, the patient's medical condition at the time of the MRI exam was blindly and separately exposed to a pediatric neurologist and a pediatric intensivist. RESULTS: During the study period, 87 patients (7.5%) underwent a brain MRI. Median age was 4 months and 13 children (14.9%) died in pediatric intensive care unit. The most common final diagnosis was postanoxic encephalopathy. Brain MRI was abnormal in 68 patients (78.2%). No serious adverse event occurred during the transport. The neurologist and the intensivist considered brain MRI as indicated during pediatric intensive care unit stay in 65 (74.7%) and 68 patients (78.2%). They deemed that brain MRI had a diagnostic contribution in 76 (87.4%) and 60 (69.0%) patients, respectively. A therapeutic change consecutive to MRI findings occurred in 19 patients (21.8%) and MRI results were associated with a decision to withdraw life-sustaining treatment in 21 patients (24.1%). CONCLUSION: Brain MRI is one component of neuromonitoring, and this study suggests a substantial diagnostic contribution, although its therapeutic impact appears limited to specific diagnoses.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".