Conservative versus operative management in stable patients with penetrating abdominal trauma: the experience of a Canadian level 1 trauma centre
Bibliographic record
Abstract
BACKGROUND: The goal of conservative management (CM) of penetrating abdominal trauma is to avoid nontherapeutic laparotomies while identifying injuries early. Factors that may predict CM failure are not well established, and the experience of CM has not been well described in the Canadian context. METHODS: We searched a Canadian level 1 trauma centre database for all penetrating abdominal traumas treated between 2004 and 2014. Hemodynamically stable patients without peritonitis and without clear indications for immediate surgery were considered potential candidates for CM, and were included in the study. We compared those who were managed with CM with those who underwent immediate operative management (OM). Outcomes included mortality and length of stay (LOS). Further analysis was performed to identify predictors of CM failure. RESULTS: A total of 72 patients with penetrating abdominal trauma were classified as potential candidates for CM. Ten patients were managed with OM, and 62 with CM, with 9 (14.5%) ultimately failing CM and requiring laparotomy. The OM and CM groups were similar in terms of age, sex, injury severity, mechanism and number of injuries. There were no deaths in either group. The LOS in the intensive care (ICU)/trauma unit was 4.8 ± 3.2 days in the OM group and 2.9 ± 2.6 days in the CM group (p = 0.039). The only predictor for CM failure was intra-abdominal fluid on computed tomography (CT) scan (odds ratio 5.3, 95% confidence interval 1.01-28.19). CONCLUSION: In select patients with penetrating abdominal trauma, CM is safe and results in a reduced LOS in the ICU/trauma unit of 1.9 days. Fluid on CT scan is a predictor for failure.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".