Indications, mortality, and long-term outcomes of 50 consecutive patients undergoing damage control laparotomy for abdominal gunshot wounds
Bibliographic record
Abstract
Introduction: Outcomes of patients undergoing damage control laparotomy (DCL) for abdominal gunshot wounds (GSWs) remains relatively unknown.The purpose of this study was to evaluate the impact of DCL on long term morbidity and survival.Methods: This retrospective study was conducted on patients undergoing a damage control laparotomy for abdominal GSWs.The data were collected using 50 consecutive trauma patients over a 4.5-year-period between August 1 st , 2004 and September 30 th , 2009.The patients were classified regarding the characteristics, such as age, perioperative physiological parameters, trauma indices, number of abdominal GSWs, critical care unit stay, hospital length of stay, morbidity, and mortality.Univariate and multivariate logistic regression was employed to compute the odds of survival and estimate the unadjusted and adjusted association between these factors.Results: According to the results, the majority of the patients were male (96%) with a mean age of 29.7 years who had a single abdominal gunshot wound (60%).Liver injuries (58%) followed by small bowel (44%), majors venous (40%), and colonic (38%) trauma were observed in the patients.The overall mortality rate was obtained at 54%.The mean length of intensive care unit stay and mean hospital length of stay were 7 and 13 days, respectively.Factors associated with a decreased odds of survival included Penetrating Abdominal Trauma Index (PATI) > 25, intra-operative blood lactate level > 8 mmol/L, and massive transfusion >10 units packed red blood cells.Conclusions: After controlling the confounding factors, a PATI score of > 25 was associated with a decreased odds of survival (OR: 0.20, P=0.04).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".