Predictors of Nosocomial Bloodstream Infections Among Critically Ill Adult Trauma Patients
Bibliographic record
Abstract
OBJECTIVE: To identify the independent predictors of nosocomial bloodstream infections (BSIs) among critically ill adult trauma patients. DESIGN: A prospective, cohort design was used to study patients who met predetermined inclusion criteria. Basic descriptive and univariate statistical analyses were performed to identify unadjusted predictors. A forward stepwise multivariate logistic regression analysis was then conducted to identify independent predictors of nosocomial BSI. SETTING: Level I university-affiliated shock trauma center. PATIENTS: Three hundred sixty-one critically ill adult trauma patients, 55 of whom developed nosocomial BSIs (15.2%). RESULTS: Data analysis of 45 variables indicated that only 9 were independent predictors of nosocomial BSI: presence of a chest tube, use of immunosuppressive agents, presence of microbial resistance, length of stay, presence of preexisting infection, percentage change of serum albumin levels, patient disposition, transfusion of 10 or more units of blood, and number of central venous catheters (CVCs) for patients who had 4 or more. The classification index of the final regression model at a cut-off point of 0.5 had a specificity of 97.4%, a sensitivity of 60%, a positive predictive value of 76.7%, a negative predictive value of 93%, and an overall precision of 91%. CONCLUSION: In this study, only 9 variables were independent predictors of nosocomial BSI. Our findings are specific to critically ill adult trauma patients and should be interpreted within the context of this particular population.
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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 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".