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Record W3022366675 · doi:10.1177/1556984520908498

Three-Dimensional Imaging Improves Accuracy in Clip Sizing During Thoracoscopic Left Atrial Appendage Exclusion

2020· article· en· W3022366675 on OpenAlexaff
Syed M. Ali Hassan, Byron H. Gottschalk, Andrés Enríquez, Gianluigi Bisleri

Bibliographic record

VenueInnovations Technology and Techniques in Cardiothoracic and Vascular Surgery · 2020
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsWestern UniversityQueen's University
Fundersnot available
KeywordsSizingMedicineAblationOcclusionCircumflexLeft atrial appendage occlusionAppendageCardiologyRadiologyAtrial fibrillationArteryInternal medicineAnatomy

Abstract

fetched live from OpenAlex

The AtriClip is an epicardially applied occlusion device for the left atrial appendage. Accurate sizing and placement of the device is essential to its success. We describe the use of 3-dimensional computed tomography imaging to aid in accurate sizing of the AtriClip device during thoracoscopic surgical ablation. This technique reduces the risk of improper sizing of the device and thus mitigates the risk of malpositioning and potential damage or compression to surrounding structures such as the circumflex coronary artery.

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)0.013
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.325
Teacher spread0.291 · 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 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

Citations1
Published2020
Admission routes1
Has abstractyes

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