Bowel obstructions and incisional hernias following trauma laparotomy and the nonoperative therapy of solid organ injuries
Bibliographic record
Abstract
BACKGROUND: Small bowel obstruction (SBO) and incisional hernia (IH) represent the most common long-term complications of laparotomy. They may also be more common among injured patients than for elective/nontrauma emergency scenarios. Unfortunately, the population-based incidence of SBO and IH following trauma laparotomy is unknown. The aim of this study was to define the long-term, population-based incidence of SBO and IH following both trauma laparotomy as well as the nonoperative therapy of solid organ injuries. METHODS: All injured patients admitted to a Level 1 trauma center (2002-2013) who underwent (1) a laparotomy or nonoperative care of (2) splenic and/or (3) hepatic injuries were linked with the Alberta Health Services Discharge Database to identify all readmissions for subsequent SBO and/or IH within the province. Standard statistical methodology was used (p < 0.05). RESULTS: Of 484 patients who underwent a trauma laparotomy, 29 (6%) and 42 (9%) required readmission for SBO and IH, respectively (0.13 SBO and 0.10 IH admissions per patient year). Patients who underwent nonoperative management of their liver and/or spleen injuries displayed long-term SBO rates of 1% (6 of 619) and 0.7% (4 of 606), respectively. The rate of SBO and IH in patients with unnecessary laparotomies was equivalent to therapeutic procedures (p = 0.183). Topical hemostatic agents, repeat laparotomies, and injury pattern did not alter SBO or IH rates (p > 0.05). CONCLUSION: The population-based, long-term rate of clinically relevant SBO and IH following trauma laparotomies is 15%. This increases to 19% on a per-admission basis. Nontherapeutic scenarios, injury pattern, topical hemostatics, and open abdomens did not alter complication rates. LEVEL OF EVIDENCE: Therapeutic study, level IV.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".