Indications for Use of Damage Control Surgery in Civilian Trauma Patients
Bibliographic record
Abstract
OBJECTIVES: To characterize and evaluate indications for use of damage control (DC) surgery in civilian trauma patients. BACKGROUND: Although DC surgery may improve survival in select, severely injured patients, the procedure is associated with significant morbidity, suggesting that it should be used only when appropriately indicated. METHODS: Two investigators used an abbreviated grounded theory method to synthesize indications for DC surgery reported in peer-reviewed articles between 1983 and 2014 into a reduced number of named, content-characteristic codes representing unique indications. An international panel of trauma surgery experts (n = 9) then rated the appropriateness (expected benefit-to-harm ratio) of the coded indications for use in surgical practice. RESULTS: The 1107 indications identified in the literature were synthesized into 123 unique pre- (n = 36) and intraoperative (n = 87) indications. The panel assessed 101 (82.1%) of these indications to be appropriate. The indications most commonly reported and assessed to be appropriate included pre- and intraoperative hypothermia (median temperature <34°C), acidosis (median pH <7.2), and/or coagulopathy. Others included 5 different injury patterns, inability to control bleeding by conventional methods, administration of a large volume of packed red blood cells (median >10 units), inability to close the abdominal wall without tension, development of abdominal compartment syndrome during attempted abdominal wall closure, and need to reassess extent of bowel viability. CONCLUSIONS: This study identified a comprehensive list of candidate indications for use of DC surgery. These indications provide a practical foundation to guide surgical practice while studies are conducted to evaluate their impact on patient care and outcomes.
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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 | 0.005 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".