Management of blunt injuries to the spleen
Bibliographic record
Abstract
BACKGROUND: Non-operative management (NOM) of blunt splenic injuries is nowadays considered the standard treatment. The present study identified selection criteria for primary operative management (OM) and planned NOM. METHODS: All adult patients with blunt splenic injuries treated at Berne University Hospital, Switzerland, between 2000 and 2008 were reviewed. RESULTS: There were 206 patients (146 men) with a mean(s.d.) age of 38.2(19.1) years and an Injury Severity Score of 30.9(11.6). The American Association for the Surgery of Trauma classification of the splenic injury was grade 1 in 43 patients (20.9 per cent), grade 2 in 52 (25.2 per cent), grade 3 in 60 (29.1 per cent), grade 4 in 42 (20.4 per cent) and grade 5 in nine (4.4 per cent). Forty-seven patients (22.8 per cent) required immediate surgery. Transfusion of at least 5 units of red cells (odds ratio (OR) 13.72, 95 per cent confidence interval 5.08 to 37.01), Glasgow Coma Scale score below 11 (OR 9.88, 1.77 to 55.16) and age 55 years or more (OR 3.29, 1.07 to 10.08) were associated with primary OM. The rate of primary OM decreased from 33.3 to 11.9 per cent after the introduction of transcatheter arterial embolization in 2005. Overall, 159 patients (77.2 per cent) qualified for NOM, which was successful in 143 (89.9 per cent). The splenic salvage rate was 69.4 per cent. In multivariable analysis age at least 40 years was the only factor independently related to failure of NOM (OR 13.58, 2.76 to 66.71). CONCLUSION: NOM of blunt splenic injuries has a low failure rate. Advanced age is independently associated with an increased failure rate.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".