The song remains the same although the instruments are changing: complications following selective non-operative management of blunt spleen trauma: a retrospective review of patients at a level I trauma centre from 1996 to 2007
Bibliographic record
Abstract
BACKGROUND: Despite a widespread shift to selective non-operative management (SNOM) for blunt splenic trauma, there remains uncertainty regarding the role of adjuncts such as interventional radiological techniques, the need for follow-up imaging, and the incidence of long-term complications. We evaluated the success of SNOM (including splenic artery embolization, SAE) for the management of blunt splenic injuries in severely injured patients. METHODS: Retrospective review (1996-2007) of the Alberta Trauma Registry and health records for blunt splenic trauma patients, aged 18 and older, with injury severity scores of 12 or greater, admitted to the Foothills Medical Centre. RESULTS: Among 538 eligible patients, 150 (26%) underwent early operative intervention. The proportion of patients managed by SNOM rose from 50 to 78% over the study period, with an overall success rate of SNOM of 87%, while injury acuity remained unchanged over time. Among SNOM failures, 65% underwent surgery within 24 hours of admission. Splenic arterial embolization (SAE) was used in only 7% of patients managed non-operatively, although at least 21% of failed SNOM had contrast extravasation potentially amenable to SAE. Among Calgary residents undergoing SNOM, hospital readmission within six months was required in three (2%), all of whom who required emergent intervention (splenectomy 2, SAE 1) and in whom none had post-discharge follow-up imaging. Overall, the use of post-discharge follow-up CT imaging was low following SNOM (10%), and thus no CT images identified occult hemorrhage or pseudoaneurysm. We observed seven cases of delayed splenic rupture in our population which occurred from five days to two months following initial injury. Three of these occurred in the post-discharge period requiring readmission and intervention. CONCLUSIONS: SNOM was the initial treatment strategy for most patients with blunt splenic trauma with 13% requiring subsequent operative intervention intended for the spleen. Cases of delayed splenic rupture occurred up to two months following initial injury. The low use of both follow-up imaging and SAE make assessment of the utility of these adjuncts difficult and adherence to formalized protocols will be required to fully assess the benefit of multi-modality management strategies.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".