Selective nonoperative management of penetrating abdominal trauma at a level 1 Canadian trauma centre: a quest for perfection
Bibliographic record
Abstract
Background: Many patients who sustain penetrating abdominal trauma can be managed nonoperatively. The Eastern Association for the Surgery of Trauma (EAST) has published guidelines on selective nonoperative management (SNOM), and this approach is well established. The purpose of this study is to assess the management of penetrating abdominal trauma, including the selection of patients for SNOM and the use of this approach, at a Canadian level 1 trauma centre. Methods: We used the Hamilton Health Sciences trauma registry to compile data on patients aged 16 years and older who sustained penetrating abdominal trauma from Jan. 1, 2011, to Dec. 31, 2017. Hemodynamically stable, nonperitonitic patients without evisceration or impalement were considered potentially eligible for SNOM. We compared the SNOM group of patients with the immediate operative (IOR) group. Our primary outcome was SNOM failure; secondary outcomes included length of stay, repeat imaging, computed tomography (CT) protocol, laparoscopy in left thoracoabdominal trauma, and nontherapeutic and negative laparotomies. Results: We included 191 patients with penetrating abdominal trauma; 123 underwent SNOM and 68 underwent IOR. Of the 68 patients in the IOR group, 4 underwent nontherapeutic laparotomies. Of the 123 patients in the SNOM group, this approach failed in 7 (5.7%). Patients who were successfully managed with SNOM had an average length of stay of 25.4 hours (7.9–43.0 h), with no repeat imaging in 34/35 (97.1%). Only 5 of the 47 patients with flank/back wounds had a CT scan that included luminal contrast. Only 3 of the 58 patients with left thoracoabdominal wounds underwent same-admission laparoscopy, all demonstrating diaphragmatic defects. Conclusion: Our study demonstrates a high rate of compliance with the EAST SNOM guidelines, including minimal failure rate of SNOM and an efficient use of resources as demonstrated by reduced length of stay and minimal use of reimaging. We identified 2 opportunities for improvement: improved use of luminal contrast CT in patients with flank/back wounds and improved use of diagnostic laparoscopy in patients with left thoracoabdominal wounds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".