Delayed hemorrhagic complications in the nonoperative management of blunt splenic trauma
Bibliographic record
Abstract
BACKGROUND: Delayed splenic rupture is the Achilles' heel of nonoperative management (NOM) for blunt splenic injury (BSI). Early computed tomographic (CT) scanning for features suggesting high risk of nonoperative failure, splenic pseudoaneurysms (SPAs), and arterial extravasation (AE), in concert with the appropriate use of splenic arterial embolization (SAE) is a viable method to reduce rates of failure of NOM. We report our 12-ear experience with a protocol for mandatory repeat CT evaluation at 48 hours and selective SAE. METHODS: A retrospective cohort analysis was performed on all consecutive adult trauma patients with BSI between 1995 and 2012. We evaluated an early/control (1995-1999) and a present/intervention (2000-2012) cohort in which SAE became available and 48-hour CT scans were implemented. RESULTS: The study included 773 patients (157 early vs. 616 present) with BSI. The proportion of patients managed nonoperatively (53% vs. 77%, p < 0.01) and overall splenic salvage rate (46% vs. 77%, p < 0.01) were improved in the present cohort. Among patients selected for NOM, there was a significant improvement in the failure rate of NOM (12% vs. 0.6%, p < 0.01) as well as in the length of hospital stay (8 days vs. 6 days, p < 0.01). Delayed development of SPA and/or AE was detected in 6% of BSI in the present cohort and was distributed among all grades of injury. CONCLUSION: The delayed development of SPA and AE is not an entirely rare event following BSI. Reevaluation with CT at 48 hours following admission and the use of SAE significantly decrease the failure rate of NOM. LEVEL OF EVIDENCE: Therapeutic study, level III.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".