Implementation of Neurocritical Care Is Associated With Improved Outcomes in Traumatic Brain Injury
Bibliographic record
Abstract
Background Traditionally, the delivery of dedicated neurocritical care (NCC) occurs in distinct NCC units and is associated with improved outcomes. Institution-specific logistical challenges pose barriers to the development of distinct NCC units; therefore, we developed a consultancy NCC service coupled with the implementation of invasive multimodal neuromonitoring, within a medical-surgical intensive care unit. Our objective was to evaluate the effect of a consultancy NCC program on neurologic outcomes in severe traumatic brain injury patients. METHODS: We conducted a single-center quasi-experimental uncontrolled pre- and post-NCC study in severe traumatic brain injury patients (Glasgow Coma Scale ≤8). The NCC program includes consultation with a neurointensivist and neurosurgeon and multimodal neuromonitoring. Demographic, injury severity metrics, neurophysiologic data, and therapeutic interventions were collected. Glasgow Outcome Scale (GOS) at 6 months was the primary outcome. Multivariable ordinal logistic regression was used to model the association between NCC implementation and GOS at 6 months. RESULTS: A total of 113 patients were identified: 76 pre-NCC and 37 post-NCC. Mean age was 39 years (standard deviation [SD], 2) and 87 of 113 (77%) patients were male. Median admission motor score was 3 (interquartile ratio, 1-4). Daily mean arterial pressure was higher (95 mmHg [SD, 10]) versus (88 mmHg [SD, 10], p<0.001) and daily mean core body temperature was lower (36.6°C [SD, 0.90]) versus (37.2°C [SD, 1.0], p=0.001) post-NCC compared with pre-NCC, respectively. Multivariable regression modelling revealed the NCC program was associated with a 2.5 increased odds (odds ratios, 2.5; 95% confidence interval, 1.1-5.3; p=0.022) of improved 6-month GOS. CONCLUSIONS: Implementation of a NCC program is associated with improved 6 month GOS in severe TBI patients.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".