Severe traumatic brain injury and acute kidney injury patients: factors associated with in-hospital mortality and unfavorable outcomes
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to identify the occurrence of AKI, and factors associated with in-hospital mortality and unfavorable outcomes in patients with severe traumatic brain injury (TBI) and acute kidney injury (AKI) severity. METHOD: A retrospective cohort study which analyzed data with severe TBI between 2013 and 2017. We examined demographic and clinical information, and outcome by in-hospital mortality, and the Glasgow Outcome Scale six months after TBI. We associated factors to in-hospital mortality and unfavorable outcome in severe TBI and AKI with an association test. RESULTS: A total of 219 patients were selected, 39.3% had an AKI, and several factors associated with AKI occurrence after severe TBI. Stage 2 or 3 of AKI (OR 12.489; 95% CI = 4.45-37.94) were independent risk for both outcomes in multivariable models, severity injury by the New Trauma Injury Severity Score (OR 0.97; 95% CI = 0.96-0.99) for mortality, and the New Injury Severity Score (OR1.07; 95% CI = 1.04-1.10) and Trauma and Injury Severity Score (OR = 0.98; 95% CI = 0.965-0.997) for unfavorable outcome. CONCLUSION: The findings of our study confirmed that AKI severity and severity of injury was also related to increased mortality and unfavorable outcome after severe TBI.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".