Trauma center performance evaluation based on costs
Bibliographic record
Abstract
BACKGROUND: In 2000, more than 50 million Americans were treated in hospitals following injury, with costs estimated at $80 billion, yet no performance indicator based on costs has been developed and validated specifically for acute trauma care. This study aimed to describe how data on costs have been used to evaluate the performance of acute trauma care hospitals. METHODS: A systematic review using MEDLINE, EMBASE, Web of Science, The Cochrane Library, CINAHL, TRIP, and ProQuest was performed in December 2012. Cohort studies evaluating hospital performance for the treatment of injury inpatients in terms of costs were considered eligible. Two authors conducted the screening and the data abstraction independently using a piloted electronic data abstraction form. Methodological quality was evaluated using seven criteria from the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement and the Downs and Black tool. RESULTS: The search retrieved 6,635 studies, of which 10 were eligible for inclusion. Nine studies were conducted in the United States and one in Europe. Six studies used patient charges as a proxy for patient costs, of which four used cost-to-charge ratios. One study estimated costs using average unit costs, and three studies were based on the real costs obtained from a hospital accounting system. Average costs per patient in 2013 US dollar varied between 2,568 and 74,435. Four studies (40%) were considered to be of good methodological quality. CONCLUSION: Studies evaluating the performance of trauma hospitals in terms of costs are rare. Most are based on charges rather than costs, and they have low methodological quality. Further research is needed to develop and validate a performance indicator based on inpatient costs that will enable us to monitor trauma centers in terms of resource use. LEVEL OF EVIDENCE: Systematic review, evidence, level III.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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".