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Record W4403845344 · doi:10.1021/acs.biomac.4c00952

External Bleeding and Advanced Biomacromolecules for Hemostasis

2024· review· en· W4403845344 on OpenAlexafffund
Sajjad Fanaee, William Austin, Mark Filiaggi, Vahid Adibnia

Bibliographic record

VenueBiomacromolecules · 2024
Typereview
Languageen
FieldMedicine
TopicHemostasis and retained surgical items
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsHemostasisHemostaticsChemistryMedicineSurgery

Abstract

fetched live from OpenAlex

Hemorrhage is a significant medical problem that has been an active area of research over the past few decades. The human body has a complex response to bleeding that leads to blood clot formation and hemostasis. Many biomaterials based on various biomacromolecules have been developed to either accelerate or improve the body’s natural response to bleeding. This review examines the mechanisms of hemostasis, types of bleeding, and the in vitro or in vivo models and techniques used to study bleeding and hemostatic materials. It provides a detailed overview of the diverse hemostatic materials, including those that are highly absorbent, wet adhesives, and those that accelerate the biochemical cascade of blood clotting. These materials are currently marketed, under preclinical testing, or being researched. In exploring the latest advancements in hemostatic technologies, this paper highlights the potential of these materials to significantly improve bleeding control in clinical and emergency situations.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.966
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.065
GPT teacher head0.398
Teacher spread0.333 · 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 designNot applicable
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

Citations14
Published2024
Admission routes2
Has abstractyes

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