Trauma center performance indicators for nonfatal outcomes
Bibliographic record
Abstract
BACKGROUND: According to Donabedian's framework, outcomes covering the following six domains should be used to evaluate health care quality: death, adverse events, readmissions to hospital, resource use, quality of life, and ability to function in daily activities. The objective of this study was to identify the nonfatal outcomes that have been used to evaluate the performance of trauma hospitals. Secondary objectives were to describe definitions and methodological quality. METHODS: We performed a scoping literature review of studies using at least one nonfatal outcome to evaluate the performance of acute care hospitals for the treatment of general trauma populations. We searched MEDLINE, EMBASE, Cochrane central, CINAHL, BIOSIS, TRIP and ProQuest databases. Methodological quality was evaluated using elements of the STROBE statement and the Downs and Black tool. RESULTS: Of 14,521 citations, 40 were eligible for inclusion. We identified 14 nonfatal outcomes as follows: (i) adverse events including complications (used in 35 evaluations), missed injuries (n = 4), reintubation (n = 2), unplanned intensive care unit admissions (n = 2), and unplanned surgeries (n = 4); (ii) resource use including hospital (n = 19), intensive care unit (n = 15), and ventilator (n = 4) length of stay, inappropriate hospital stay (n = 1), and potentially unnecessary care (n = 1); (iii) hospital readmissions (n = 4); and (iv) ability to function in daily activities including functional capacity (n = 2), and discharge destination (n = 3). No measures of quality of life were identified. There was high heterogeneity in the definitions used. Only 18% of studies had high methodological quality. CONCLUSION: Among recommended domains of nonfatal outcomes, adverse events and resource use were frequently used to evaluate trauma care, readmissions and function in daily activities were rarely used, and quality of life was never used. In addition, definitions of nonfatal outcomes were variable, and methodological quality was low. There is a need to develop valid and reliable performance indicators based on each domain of Donabedian's framework to evaluate trauma care.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".