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Record W4405535952 · doi:10.1016/j.jvssci.2024.100277

Moving away from metal: Step toward the future with bioresorbable vascular scaffolds and novel antiproliferative agents

2024· article· en· W4405535952 on OpenAlexaff
Blair E. Warren, Kongteng Tan, Dheeraj K. Rajan, Miranda Witheford, Sean A. Crawford, Arash Jaberi, Sebastian Mafeld

Bibliographic record

VenueJVS Vascular Science · 2024
Typearticle
Languageen
FieldMedicine
TopicPeripheral Artery Disease Management
Canadian institutionsToronto General HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsNanotechnologyBiomedical engineeringMaterials scienceMedicine

Abstract

fetched live from OpenAlex

Background: Peripheral arterial disease (PAD) is a common source of morbidity and mortality globally and is expected to raise increase in prevalence. Many endovascular techniques exist to manage PAD; however, there remains room for improvement, especially as it relates to below-the-knee vessels. Recent evidence and devices are leading to a resurgence of interest in bioresorbable vascular scaffolds and the -limus family of antiproliferative drugs in the PAD treatment space. Methods: This nonsystematic review examines emerging technology for treatment of PAD with a specific focus on below-the-knee vessels and bioresorbable vascular scaffolds. Additional emerging and early technology such as novel delivery platforms are also briefly discussed with directions of future research highlighted. Results: Bioresorbable vascular scaffold biomechanics and history are highlighted. Foundational knowledge of antiproliferative agents and evolving agents in peripheral vascular disease are also described. Conclusions: Bioresorbable vascular scaffolds are an additional endovascular tool for the treatment of peripheral vascular disease. The integration with an antiproliferative agent may result in improved patency and performance; however, there is a paucity of data in the literature at present.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.474
Threshold uncertainty score0.561

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.014
GPT teacher head0.254
Teacher spread0.240 · 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 designObservational
Domainnot available
GenreEmpirical

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
Published2024
Admission routes1
Has abstractyes

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