Prediction of raised intracranial pressure complicating severe traumatic brain injury in children: Implications for trial design*
Bibliographic record
Abstract
OBJECTIVES: To describe current patterns of management of raised intracranial pressure (ICP) in traumatic brain injury relevant to clinician buy-in to possible randomized controlled trials of treatments of raised ICP. To examine the feasibility of early identification of children at sufficient risk of developing raised ICP to permit a uniform approach between centers to the initiation of ICP monitoring. This would permit quantification of ICP elevation and enrollment as appropriate to randomized controlled trials of raised ICP interventions. DESIGN: Logistic regression modeling of death before pediatric intensive care unit discharge and decision tree and logistic regression of development of raised ICP through analysis of a prospectively collected, standardized, national data set. SETTING: Pediatric intensive care units in the United Kingdom and Eire. PATIENTS: Patients were 501 children <16 yrs of age primarily admitted to intensive care unit for management of traumatic brain injury in the United Kingdom and Eire between February 2001 and August 2003. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The data analyzed included demographic, acute physiologic, and cranial imaging variables. Death was associated with both raised ICP and the nonmeasurement of ICP. In a subset of 199 patients, an empirically derived decision rule predicted the development of raised ICP at any point during ICU admission with sensitivity of 73% and specificity of 74% (positive predictive value 82% and negative predictive value 63%). Logistic regression modeling performed comparably. The decision rule also predicted raised ICP in 20% of children not undergoing ICP monitoring. CONCLUSIONS: Simple models based on early clinical data may predict the development of raised ICP sufficiently well to encourage a consistent approach between centers to initiation of ICP monitoring. We estimate studies designed to detect reductions in ICU mortality will require >320 children per arm, although this figure may be higher if more conservative assumptions are made.
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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 | 0.165 | 0.307 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".