Sepsis in Traumatic Brain Injury: Epidemiology and Outcomes
Bibliographic record
Abstract
BACKGROUND: Traumatic brain injury (TBI) is a leading cause of death and disability. Risk factors for in-hospital mortality include older age, co-morbidity, and TBI severity. Few studies have investigated the role of sepsis in individuals with TBI. METHODS: We studied adult patients with TBI admitted to intensive care over a 5-year period. Patient characteristics were identified by linking clinical and administrative databases. Charts of individuals with TBI and sepsis were manually reviewed. Predictors of ICU and hospital mortality were identified using logistic regression modeling. RESULTS: Four hundred eighty-six individuals with TBI were admitted to intensive care. Sixteen (3.3%) developed sepsis. Pneumonia was the most common source (94%). Staphylococcus aureus was the most common pathogen (75%). ICU lengths of stay (LOS) (12.2 days [interquartile range (IQR) 4.4-23.5] versus 3.7 days [IQR 1.7-8.2]; p < 0.001) and hospital LOS (28.0 days [IQR 11.8-41.4] versus 15.3 days [IQR 5.0-30.9]; p = 0.017) were longer in patients with TBI and sepsis. Sepsis was not associated with ICU (adjusted odds ratio [aOR] 0.51; 95%CI 0.12-2.27; p = 0.38) or hospital (aOR 0.78; 95% CI 0.21-2.96; p = 0.78) mortality, though age (aOR 1.02; 95% CI 1.00-1.04; p = 0.014 for hospital mortality), severe TBI (aOR 3.71; 95% CI 1.52-9.08; p = 0.004 for ICU mortality and 4.10; 95% CI 1.95-8.65; p < 0.001 for hospital mortality), and APACHE II score (aOR 1.19; 95% CI 1.11-1.28; p < 0.001 for ICU mortality and 1.22; 95% CI 1.14-1.31; p < 0.001 for hospital mortality) were. CONCLUSION: Sepsis in patients with TBI was not associated with mortality; however, sepsis was associated with increased health care utilization (ICU and hospital LOS).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| 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".