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Record W4282943497 · doi:10.1039/d2tb00546h

Hemostatic biomaterials to halt non-compressible hemorrhage

2022· review· en· W4282943497 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Materials Chemistry B · 2022
Typereview
Languageen
FieldMedicine
TopicHemostasis and retained surgical items
Canadian institutionsMcGill University
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMaterials scienceHemostatic AgentHemostasisNanotechnologyChemical engineeringBiomedical engineeringMedicineInternal medicineEngineering

Abstract

fetched live from OpenAlex

Non-compressible hemorrhage is an unmet clinical challenge, which occurs in inaccessible sites in the body where compression cannot be applied to stop bleeding. Current treatments reliant on blood transfusion are limited in efficacy and complicated by blood supply (short shelf-life and high cost), immunogenicity and contamination risks. Alternative strategies based on hemostatic biomaterials exert biochemical and/or mechanical cues to halt hemorrhage. The biochemical hemostats are built upon native coagulation cascades, while the mechanical hemostats use mechanical efforts to stop bleeding. This review covers the design principles and applications of such hemostatic biomaterials, following an overview of coagulation mechanisms and clot mechanics. We present how biochemical strategies modulate coagulation and fibrinolysis, and also mechanical mechanisms such as absorption, agglutination, and adhesion to achieve hemostasis. We also outline the challenges and immediate opportunities to provide comprehensive guidelines for the rational design of hemostatic biomaterials.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.827
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0180.000

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 head0.060
GPT teacher head0.356
Teacher spread0.295 · 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