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Record W6958342942 · doi:10.6084/m9.figshare.23866886

A contemporary systematic review of the complications associated with SURGICEL

2023· article· en· W6958342942 on OpenAlexaff

Bibliographic record

VenueFigshare · 2023
Typearticle
Languageen
FieldMedicine
TopicUreteral procedures and complications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMEDLINEData extractionSystematic reviewMedical literatureUrinary systemCINAHL

Abstract

fetched live from OpenAlex

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 <i>n</i> = 93, others <i>n</i> = 7149). 67/70 of the included studies reported complications when SURGICEL was used intraoperatively. Reported complications included: SURGICEL induced masses (granulomas, abscesses, hematomas, cysts) (<i>n</i> = 25), hemorrhagic complications (<i>n</i> = 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.932
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.094
GPT teacher head0.300
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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