Complications to evaluate adult trauma care
Bibliographic record
Abstract
BACKGROUND: Complications affect up to 37% of patients hospitalized for injury and increase mortality, morbidity, and costs. One of the keys to controlling complications for injury admissions is to monitor in-hospital complication rates. However, there is no consensus on which complications should be used to evaluate the quality of trauma care. The objective of this study was to develop a consensus-based list of complications that can be used to assess the acute phase of adult trauma care. METHODS: We used a three-round Web-based Delphi survey among experts in the field of trauma care quality with a broad range of clinical expertise and geographic diversity. The main outcome measure was median importance rating on a 5-point Likert scale (very low to very high); complications with a median of 4 or greater and no disagreement were retained. A secondary measure was the perceived quality of information on each complication available in patient files. RESULTS: Of 19 experts invited to participate, 17 completed the first (brainstorming) round and 16 (84%) completed all rounds. Of 73 complications generated in Round 1, a total of 25 were retained including adult respiratory distress syndrome, hospital-acquired pneumonia, sepsis, acute renal failure, deep vein thrombosis, pulmonary embolism, wound infection, decubitus ulcers, and delirium. Of these, 19 (76%) were perceived to have high-quality or very high-quality information in patient files by more than 50% of the panel members. CONCLUSION: This study proposes a consensus-based list of 25 complications that can be used to evaluate the quality of acute adult trauma care. These complications can be used to develop an informative and actionable quality indicator to evaluate trauma care with the goal of decreasing rates of hospital complications and thus improving patient outcomes and resource use. DRG International Classification of Diseases codes are provided.
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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 | 0.001 |
| 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".