Infection Rates, Fevers, and Associated Factors in Pediatric Severe Traumatic Brain Injury
Bibliographic record
Abstract
Abstract Infections can increase medical costs and worsen patient outcomes. Our aims in pediatric severe traumatic brain injury (sTBI) patients were to determine the infection and fever rates, and to report on associated clinical, imaging, treatment, and outcome factors. We included 180 sTBI patients (presedation Glasgow Coma Scale ≤ 8 and Maximum Abbreviated Injury Scale ≥ 4) admitted to our pediatric intensive care unit. Overall, 17% of sTBI patients (n=30 of 180) developed 36 infections, consisting primarily of urinary tract infections (UTIs; n=13 of 36) and ventilator-associated pneumonias (n=11 of 36). Most infections were nosocomial, occurring >2 days after admission. Fever was found in 36% of sTBI patients during the first few hospital days, but fewer than 7% of patients had infections. Infections occurred more frequently in sTBI patients who were older, heavier, and with a higher injury severity score (ISS; p<0.05). Admission head computed tomography imaging abnormalities (subarachnoid hemorrhage, intraventricular hemorrhage, and diffuse axonal injury), placement of an intracranial pressure (ICP) monitor, and administration of ICP-lowering therapies (hypertonic saline, mannitol, and thiopental) were associated with infections (p<0.05). Those with infections had fewer ventilator-free days, greater hospital lengths of stays, and were less likely to be discharged home. Logistic regression demonstrated that infections were independently associated with use of hypertonic saline (odds ratio [OR], 4.46; p=0.001) and higher ISS (OR, 1.05; p=0.028). In summary, infections were prevalent in sTBI patients and were associated with greater head-imaging abnormalities and use of ICP-lowering therapies. Hypertonic saline administration was strongly associated with infection, but further analyses are required to determine the nature of this relationship. Fever was a poor indicator of infection after sTBI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".