A contemporary systematic review of the complications associated with SURGICEL
Bibliographic record
Abstract
This review aims to summarize the findings from recent literature (2010–2022) reporting on complications that resulted from the surgical use of SURGICEL for intraoperative hemostasis. A literature search was conducted using the MEDLINE (OVID), Embase, and Cochrane Central Register of Controlled Trials – CENTRAL (OVID) databases. The studies were sorted into case reports and other study types for data extraction. Covidence was used for data extraction and statistics were descriptive. Of the total 560 articles screened, 73 papers were selected for a full-text review and 70 studies were included in this review. A total of 7,242 participants were included in the studies (case studies n = 93, others n = 7149). 67/70 of the included studies reported complications when SURGICEL was used intraoperatively. Reported complications included: SURGICEL induced masses (granulomas, abscesses, hematomas, cysts) (n = 25), hemorrhagic complications (n = 12), masses misdiagnosed as tumors, cardiovascular, nervous system, and hepatobiliary complications, pain, and infections. Other complications included: fistulas, erectile dysfunction, chorioamnionitis, swelling, urinary leak, renal failure, and anaphylaxis. Publications reporting on complications associated with the use of SURGICEL intraoperatively have continued to emerge. Future studies should compare how the types and rates of complications compare between SURGICEL and alternative hemostatic agents.
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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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".