The impacts of different embolization techniques on splenic artery embolization for blunt splenic injury: a systematic review and meta-analysis
Bibliographic record
Abstract
Splenic artery embolization (SAE) has been an effective adjunct to the Non-operative management (NOM) for blunt splenic injury (BSI). However, the optimal embolization techniques are still inconclusive. To further understand the roles of different embolization locations and embolic materials in SAE, we conducted this system review and meta-analyses. Clinical studies related to SAE for adult patients were researched in electronic databases, included PubMed, Embase, ScienceDirect and Google Scholar Search (between October 1991 and March 2013), and relevant information was extracted. To eliminate the heterogeneity, a sensitivity analysis was conducted on two reduced study sets. Then, the pooled outcomes were compared and the quality assessments were performed using Newcastle-Ottawa Scale (NOS). The SAE success rate, incidences of life-threatening complications of different embolization techniques were compared by χ 2 test in 1st study set. Associations between different embolization techniques and clinical outcomes were evaluated by fixed-effects model in 2nd study set. Twenty-three studies were included in 1st study set. And then, 13 of them were excluded, because lack of the necessary details of SAE. The remaining 10 studies comprised 2nd study set, and quality assessments were performed using NOS. In 1st set, the primary success rate is 90.1% and the incidence of life-threatening complications is 20.4%, though the cases which required surgical intervention are very few (6.4%). For different embolization locations, there was no obvious association between primary success rate and embolization location in both 1st and 2nd study sets (P > 0.05). But in 2nd study set, it indicated that proximal embolization reduced severe complications and complications needed surgical management. As for the embolic materials, the success rate between coil and gelfoam is not significant. However, coil is associated with a lower risk of life-threatening complications, as well as less complications requiring surgical management. Different embolization techniques affect the clinical outcomes of SAE. The proximal embolization is the best option due to the less life-threatening complications. For commonly embolic material, coil is superior to gelfoam for fewer severe complications and less further surgery management.
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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 | 0.018 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.045 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".