Impact of Interhospital Transfer on Outcomes for Trauma Patients: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Evidence suggests that there may be an association between transfer status (direct admission or interhospital transfer) and outcomes in trauma patients. The purpose of this study was to systematically review the current evidence of the association between transfer status and outcomes for patients. METHODS: Systematic search of Medline and EMBASE databases to identify eligible control trials or observational studies that examined the impact of transfer status on trauma patient outcomes. Data were extracted on study design, quality, participants, outcomes, and risk estimates reported. Pooled odds ratio based on data from retrieved studies was calculated using a random effect model. RESULTS: Thirty-six observational studies were identified. There were no significant differences in length of stay (LOS) between transfer and direct admissions although costs were marginally higher for transferred patients, (relative increase, 1.09; 95% confidence interval, 1.08-1.09). We found no significant association between transfer status (transfer vs. direct) and in-hospital mortality (pooled odds ratio, 1.06; 95% confidence interval, 0.90-1.25); however, heterogeneity of the studies was high (I2 = 82%). CONCLUSION: Available evidence suggests there is no difference in mortality between transfer and direct admissions. However, the significant heterogeneity across studies precludes deriving any definitive conclusions regarding the impact of interhospital transfer on mortality after major trauma. Moreover, most studies excluded patients dying at outlying hospitals, which may underestimate the association of transfer status with mortality. Prospective studies that address the limitations of the current evidence, including use of population-based trauma registries, are warranted to establish whether the process of interhospital transfer to higher level care when compared with direct admission to a trauma center negatively impacts clinical outcomes for trauma patients.
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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 | 0.011 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".