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Record W2006542102 · doi:10.1042/cs20070228

Preventing restenosis after angioplasty: a multistage approach

2008· review· en· W2006542102 on OpenAlexaff
Ramin Zargham

Bibliographic record

VenueClinical Science · 2008
Typereview
Languageen
FieldMedicine
TopicCoronary Interventions and Diagnostics
Canadian institutionsMcGill University
Fundersnot available
KeywordsRestenosisNeointimaAngioplastyCardiologyMedicineTunica mediaLumen (anatomy)BalloonInternal medicineTunica intimaVascular smooth muscleArteryNeointimal hyperplasiaStentSmooth muscle

Abstract

fetched live from OpenAlex

Arterial reconstruction procedures, including balloon angioplasty, stenting and coronary artery bypass, are used to restore blood flow in atherosclerotic arteries. Restenosis of these arteries has remained a major limitation of the application of these procedures, especially in the case of balloon angioplasty. Post-angioplasty restenosis results from two major processes: neointimal formation and constrictive remodelling. Neointimal formation is initiated by arterial injury with a resultant loss of contractile phenotype in tunica media, leading to VSMC [vascular SM (smooth muscle) cell] migration from the tunica media to the intima. Migrated VSMCs contribute to the intimal thickening by the excessive synthesis of ECM (extracellular matrix) and proliferation. However, increased neointimal mass is not solely responsible for luminal narrowing. Inward constrictive remodelling is also considered as a major cause of delayed failure of angioplasty. At later stages after angioplasty, the increase in contractile forces leads to lumen narrowing. Recent studies show that SM contractile proteins are re-expressed in the neointima, concomitant with late lumen loss. Therefore one important question is whether the restoration of contractile phenotype, which can suppress VSMC migration, is favourable or detrimental. In this review, the importance of viewing restenosis as a multistage process is discussed. Different stages of restenosis occur in a sequential manner and are related to each other, but in each stage a different strategy should be taken into consideration to reduce restenosis. Defining the role of each process not only reshapes the current concept, but also helps us to target restenosis with more efficacy.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.177
GPT teacher head0.475
Teacher spread0.297 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations65
Published2008
Admission routes1
Has abstractyes

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