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Record W4396845246 · doi:10.15420/japsc.2023.58

Stent Ablation by Rotational Atherectomy for Management of Resistant Lesions within and across the Stent Struts in Coronary Arteries

2024· article· en· W4396845246 on OpenAlexaff
Vikas Kadiyala, Khung Keong Yeo

Bibliographic record

VenueJournal of Asian Pacific Society of Cardiology · 2024
Typearticle
Languageen
FieldMedicine
TopicCoronary Interventions and Diagnostics
Canadian institutionsAstraZeneca (Canada)
Fundersnot available
KeywordsStentCoronary arteriesAblationMedicineRadiologyInternal medicineCardiologyArtery

Abstract

fetched live from OpenAlex

Background: Non-dilatable stent underexpansion has limited treatment options. Stent ablation by rotational atherectomy (RA) for the management of non-dilatable coronary stents is an off-label strategy. The principle of stent ablation by RA of underexpanded stent relies on modification of both stent and underlying calcium. This study aims to describe and compare the use of RA for non-dilatable lesions in two anatomical categories: group 1 (within the stent struts) and group 2 (across the stent struts). Methods: A total of seven patients who had undergone stent ablation by RA were analysed. Indications for revascularisation were acute coronary syndrome or chronic stable angina. Two groups are described: RA within underexpanded stents (group 1) and across the stent struts (across stented bifurcation; group 2). Results: The burr:artery ratio was 0.6 ± 0.1 in group 1 and 0.5 in group 2 (p=0.24). The maximum rotational speed was 154,400 ± 2,500 and 155,000 rpm in the two groups. The total number of RA runs was 3.6 ± 0.90 in group 1 and 7.0 ± 2.8 in group 2, respectively, p=0.04. Procedural success was achieved in all cases with no complications. Conclusion: RA is a feasible bailout option for resistant non-dilatable lesions within and across the stent struts.

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 score0.485
Threshold uncertainty score0.216

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.021
GPT teacher head0.305
Teacher spread0.284 · 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

Explore more

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