Predicting Nosocomial Bloodstream Infections Using Surrogate Markers of Injury Severity
Bibliographic record
Abstract
BACKGROUND: Injury severity indices are numerical scores that are utilized to predict nosocomial bloodstream infections (BSI) in critically ill patients. However, surrogate markers of injury severity (SMIS) may be more clinically meaningful than these commonly used numerical injury severity indices with respect to the control and prevention of nosocomial BSI. OBJECTIVE: The purpose of this study was to demonstrate the clinical and research implications of using the SMIS in predicting nosocomial BSI. METHOD: A prospective nonexperimental cohort study was conducted on 361 critically ill trauma patients. Three logistic regression models were examined for their clinical relevance and statistical parsimony. The first model included the Injury Severity Score (ISS) and 5 other independent predictors, and excluded the SMIS. The second model included all study variables. The third model excluded the ISS. RESULTS: The analysis suggested that number of blood units transfused, number of central venous catheters inserted, and use of chest tube(s) were the SMIS. The ISS was found to be an independent predictor of nosocomial BSI only when the SMIS were not included in the model. The model that included the SMIS and excluded the ISS explained the highest variance in nosocomial BSI and had the best negative predictive value (93%). DISCUSSION: Clinicians can use knowledge of SMIS to develop interventions that minimize the risk of nosocomial BSI. Hence, the SMIS can serve not only as a prediction tool but also as a way to enhance control and prevention strategies for BSI.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".