MétaCan
Menu
Back to cohort

Tailored-Biomaterials Based Potential Strategies for Cardiovascular Disease

2024· review· en· W4401934513 on OpenAlexaff
Sukhwinder K. Bhullar, Haimanti Mondal, John Thomas, Duygu Gazioğlu Rüzgar, Natarajan Chandrasekaran, Amitava Mukherjee, Martin Byung‐Guk Jun, Stephanie M. Willerth

Bibliographic record

VenueDiscovery Medicine · 2024
Typereview
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsUniversity of VictoriaUniversity of British ColumbiaSt. Boniface Hospital
Fundersnot available
KeywordsDiseaseMedicineCardiovascular healthIntensive care medicineDisease treatmentBiomaterialDrug deliveryBiomedical engineeringNanotechnologyPathology

Abstract

fetched live from OpenAlex

Cardiovascular disease is a significant health concern worldwide, and varied effective treatment and prevention methods have been developed. Among these, tailored biomaterials-based strategies such as stents, scaffolds, patches, and drug delivery systems have emerged as a promising avenue. These devices are designed to match the mechanical and biological mechanisms of the cardiovascular system, ensuring optimal performance and compatibility. By effectively treating or preventing cardiovascular diseases, these devices have the potential to improve patient health outcomes significantly. They can restore blood flow by addressing blocked arteries and regenerate damaged cardiac tissue by delivering bioactive agents or cells directly to the affected area in a targeted, sustained, and controllable manner. Therefore, the objective of this article is to summarize the available evidence on these tailored biomaterial-based tunable cardiovascular devices. This knowledge can help to transform cardiovascular medicine for the treatment or prevention of cardiovascular disease and restore cardiac function to improve patients' quality of life.

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)0.001
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 score0.816
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.061
GPT teacher head0.352
Teacher spread0.291 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueDiscovery MedicineSame topicTissue Engineering and Regenerative MedicineFrench-language works237,207