MétaCan
Menu
Back to cohort
Record W3025416820 · doi:10.1080/24748706.2020.1742406

Technical Considerations and Pitfalls of BASILICA: Bioprosthetic or Native Aortic Scallop Intentional Laceration to Prevent Iatrogenic Coronary Artery Obstruction

2020· article· en· W3025416820 on OpenAlexaff
Ikki Komatsu, Harindra Wijeysandera, Sam Radharkrisnan, Brian Whisenant, Matheus Simonato, Albert Chen, G. Burkhard Mackensen, Mark Reisman, Christian Spies, Kashish Goel, Mohamed Abdel‐Wahab, Danny Dvir

Bibliographic record

VenueStructural Heart · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsRoyal Columbian HospitalSunnybrook Health Science Centre
Fundersnot available
KeywordsScallopMedicineArteryCardiologyInternal medicineSurgeryFisheryBiology

Abstract

fetched live from OpenAlex

Coronary obstruction is a major complication of transcatheter aortic valve replacement (TAVR). Rapid and growing demand for TAVR procedures is accompanied by interest in techniques that may prevent this life-threatening adverse event. BASILICA (Bioprosthetic or Native Aortic Scallop Intentional Laceration to Prevent Iatrogenic Coronary Artery Obstruction) is a transcatheter technique that may effectively prevent coronary obstruction. Formal proctorship and assistance by operators already experienced with the performance of BASILICA is beneficial to ensure good clinical outcomes. This review describes the BASILICA procedure and outlines selected technical considerations and pitfalls. Abbreviations TAVR: transcatheter aortic valve replacement; BASILICA: Bioprosthetic or Native Aortic Scallop Intentional Laceration to Prevent Iatrogenic coronary Artery Obstruction; THV: transcatheter heart valve; VTC: virtual valve to coronary distance; ViV: valve-in-valve; VIVID: valve in valve international data.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.003
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 head0.020
GPT teacher head0.332
Teacher spread0.311 · 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 designCase report
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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueStructural HeartSame topicCardiac Valve Diseases and TreatmentsFrench-language works237,207