Structure, Process, and Culture of Intensive Care Units Treating Patients with Severe Traumatic Brain Injury: Survey of Centers Participating in the American College of Surgeons Trauma Quality Improvement Program
Bibliographic record
Abstract
Outcome after severe traumatic brain injury (TBI) differs substantially between hospitals. Explaining this variation begins with understanding the differences in structures and processes of care, particularly at intensive care units (ICUs) where acute TBI care takes place. We invited trauma medical directors (TMDs) from 187 centers participating in the American College of Surgeons Trauma Quality Improvement Program (ACS TQIP) to complete a survey. The survey domains included ICU model, type, availability of specialized units, staff, training programs, standard protocols and order sets, approach to withdrawal of life support, and perceived level of neurosurgeons' engagement in the ICU management of TBI. One hundred forty-two TMDs (76%) completed the survey. Severe TBI patients are admitted to dedicated neurocritical care units in 52 hospitals (37%), trauma ICUs in 44 hospitals (31%), general ICUs in 34 hospitals (24%), and surgical ICUs in 11 hospitals (8%). Fifty-seven percent are closed units. Board-certified intensivists directed 89% of ICUs, whereas 17% were led by neurointensivists. Sixty percent of ICU directors were general surgeons. Thirty-nine percent of hospitals had critical care fellowships and 11% had neurocritical care fellowships. Fifty-nine percent of ICUs had standard order sets and 61% had standard protocols specific for TBI, with the most common protocol relating to intracranial pressure management (53%). Only 43% of TMDs were satisfied with the current level of neurosurgeons' engagement in the ICU management of TBI; 46% believed that neurosurgeons should be more engaged; 11% believed they should be less engaged. In the largest survey of North American ICUs caring for TBI patients, there is substantial variation in the current approaches to ICU care for TBI, highlighting multiple opportunities for comparative effectiveness 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".