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Record W2785833800 · doi:10.14740/cr627w

Acute Stent Loss and Its Retrieval of a Long, Tapering Morph Stent in a Tortuous, Calcified Lesion

2018· article· en· W2785833800 on OpenAlexvenueaboutno aff
Santosh Kumar Sinha, Anupam Mahrotra, Nishant Kumar Abhishekh, Mahmodula Razi, Puneet Aggarwal, Sunil Kumar Tripathi, Lokendra Rekwaal, Anupam Singh

Bibliographic record

VenueCardiology Research · 2018
Typearticle
Languageen
FieldMedicine
TopicCoronary Interventions and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineStentTIMICardiologyStenosisCircumflexFractional flow reserveRight coronary arteryBalloonCoronary stentArteryInternal medicineLesionRadiologyTaperingPercutaneous coronary interventionMyocardial infarctionSurgeryCoronary angiographyRestenosis

Abstract

fetched live from OpenAlex

A 72-year-old male with diabetes and smoking as coronary risk factors was evaluated for chronic stable angina - Canadian Cardiovascular Society III - despite guideline directed medical treatment which revealed a diffuse, tortuous, calcified narrowing (90% stenosis) in left circumflex (LCx) coronary artery. After predilatation, a 3.0 - 2.5 × 60 mm BioMime Morph stent - long tapering stent (Sirolimus eluting stent, Meril life Sciences, India) - was tracked which failed and dislodged to right deep femoral artery during its pullback. It was successfully retrieved by EN snare: 6 - 10 mm (Merit Medical, USA) by contralateral femoral approach. Lesion was further dilated and successfully stented with another 3.0 - 2.5 × 60 mm BioMime Morph stent at 10 atm pressure showing proper stents expansion with TIMI-3 coronary flow. Our case highlights trackibility issues and importance of adequate lesion preparation before stent deployment in a tortuous and calcified vessel especially with very long stent. To the best of our knowledge, this is the first such case report demonstrating dislodgement and successful retrieval of long, tapered Morph stent.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.303

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.145
GPT teacher head0.438
Teacher spread0.293 · 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

Citations5
Published2018
Admission routes2
Has abstractyes

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