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Record W4236623496 · doi:10.31219/osf.io/mbv6r

Utility of fibrin sealants and the road ahead in plastic surgery: a scoping review

2021· review· en· W4236623496 on OpenAlexaff
Mohammadali Saffarzadeh, Taylor Kandler, Safal Parhar

Bibliographic record

Venuenot available
Typereview
Languageen
FieldMedicine
TopicHemostasis and retained surgical items
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGrey literatureMEDLINESystematic reviewMedicinePopularityNarrative reviewSealantIntensive care medicineRisk analysis (engineering)SurgeryPsychologyPolitical science

Abstract

fetched live from OpenAlex

Objective:The objective of this scoping review is to examine the extent of literature on the utility and safety of various types of fibrin sealants in plastic surgery. Considerations such as consent, and cost profile will also be reviewed and identified as future research opportunities.Introduction:Since the approval of fibrin sealants in Europe in the 1970s, they have found their niche in various types of surgical operations, and their utility has led to a rise in popularity. However, the clinical applications of fibrin sealants are not fully synthesized, and important ethical and financial considerations are rarely discussed.Inclusion criteria:Studies reporting the application of commercially available fibrin sealants in plastic surgery procedures on humans of all age groups were included in this review. Additionally, published abstracts and all opinion pieces including commentaries and narrative reviews were exempt.Methods:The key information sources that will be searched are the following: Ovid MEDLINE, Cochrane Central Register of Controlled Trials (CENTRAL), Cochrane Database of Systematic Reviews, Embase, Web of Science Core Collection, and LILACS and grey literature sources. There are no limits placed on the searches. Each publication will be screened by two authors, and any conflict in the selection decision will be resolved by a third author to limit bias.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.685
Threshold uncertainty score0.596

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.001
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)0.0010.000

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 head0.135
GPT teacher head0.405
Teacher spread0.270 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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
Published2021
Admission routes1
Has abstractyes

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