Intracranial Pressure Monitoring in Severe Traumatic Brain Injury: Results from the American College of Surgeons Trauma Quality Improvement Program
Bibliographic record
Abstract
Although existing guidelines support the utilization of intracranial pressure (ICP) monitoring in patients with traumatic brain injury (TBI), the evidence suggesting benefit is limited. To evaluate the impact on outcome, we determined the relationship between ICP monitoring and mortality in centers participating in the American College of Surgeons Trauma Quality Improvement Program (TQIP). Data on 10,628 adults with severe TBI were derived from 155 TQIP centers over 2009-2011. Random-intercept multilevel modeling was used to evaluate the association between ICP monitoring and mortality after adjusting for important confounders. We evaluated this relationship at the patient level and at the institutional level. Overall mortality (n=3769) was 35%. Only 1874 (17.6%) patients underwent ICP monitoring, with a mortality of 32%. The adjusted odds ratio (OR) for mortality was 0.44 [95% confidence interval (CI), 0.31-0.63], when comparing patients with ICP monitoring to those without. It is plausible that patients receiving ICP monitoring were selected because of an anticipated favorable outcome. To overcome this limitation, we stratified hospitals into quartiles based on ICP monitoring utilization. Hospitals with higher rates of ICP monitoring use were associated with lower mortality: The adjusted OR of death was 0.52 (95% CI, 0.35-0.78) in the quartile of hospitals with highest use, compared to the lowest. ICP monitoring utilization rates explained only 9.9% of variation in mortality across centers. Results were comparable irrespective of the method of case-mix adjustment. In this observational study, ICP monitoring utilization was associated with lower mortality. However, variability in ICP monitoring rates contributed only modestly to variability in institutional mortality rates. Identifying other institutional practices that impact on mortality is an important area for future research.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".