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Record W4296704750 · doi:10.1093/rb/rbac063

Recent advances in biopolymer-based hemostatic materials

2022· review· en· W4296704750 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueRegenerative Biomaterials · 2022
Typereview
Languageen
FieldMedicine
TopicHemostasis and retained surgical items
Canadian institutionsUniversity of Victoria
FundersNational Heart, Lung, and Blood Institute
KeywordsBiopolymerHemostatic AgentHemostasisSelf-healing hydrogelsNatural polymersBiocompatibilityNanotechnologyPolymerBiomedical engineeringChemistryMaterials scienceMedicineSurgeryPolymer chemistry

Abstract

fetched live from OpenAlex

Hemorrhage is the leading cause of trauma-related deaths, in hospital and prehospital settings. Hemostasis is a complex mechanism that involves a cascade of clotting factors and proteins that result in the formation of a strong clot. In certain surgical and emergency situations, hemostatic agents are needed to achieve faster blood coagulation to prevent the patient from experiencing a severe hemorrhagic shock. Therefore, it is critical to consider appropriate materials and designs for hemostatic agents. Many materials have been fabricated as hemostatic agents, including synthetic and naturally derived polymers. Compared to synthetic polymers, natural polymers or biopolymers, which include polysaccharides and polypeptides, have greater biocompatibility, biodegradability and processibility. Thus, in this review, we focus on biopolymer-based hemostatic agents of different forms, such as powder, particles, sponges and hydrogels. Finally, we discuss biopolymer-based hemostatic materials currently in clinical trials and offer insight into next-generation hemostats for clinical translation.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.977
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0130.000

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 head0.104
GPT teacher head0.394
Teacher spread0.290 · 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