Nurse-Sensitive Indicators as Predictors of Trauma Patient Discharge Disposition
Bibliographic record
Abstract
BACKGROUND: About 3.5 million trauma patients are hospitalized every year, but 35%-40% require further care after discharge. Nurses' ability to affect discharge disposition by minimizing the occurrence of nurse-sensitive indicators (catheter-associated urinary tract infection [CAUTI], central line-associated bloodstream infection [CLABSI], and hospital-acquired pressure injury [HAPI]) is unknown. These indicators may serve as surrogate measures of quality nursing care. OBJECTIVE: The purpose of this study was to determine whether nursing care, as represented by three nurse-sensitive indicators (CAUTI, CLABSI, and HAPI), predicts discharge disposition in trauma patients. METHODS: This study was a secondary analysis of the 2021 National Trauma Data Bank. We performed logistic regression analyses to determine the predictive effects of CAUTI, CLABSI, and HAPI on discharge disposition, controlling for participant characteristics. RESULTS: A total of n = 29,642 patients were included, of which n = 21,469 (72%) were male, n = 16,404 (64%) were White, with a mean (SD) age of 44 (14.5) and mean (SD) Injury Severity Score of 23.2 (12.5). We created four models to test nurse-sensitive indicators, both individually and compositely, as predictors. While CAUTI and HAPI increased the odds of discharge to further care by 1.4-1.5 and 2.1 times, respectively, CLABSI was not a statistically significant predictor. CONCLUSIONS: Both CAUTI and HAPI are statistically significant predictors of discharge to further care for patients after traumatic injury. High-quality nursing care to prevent iatrogenic complications can improve trauma patients' long-term outcomes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".