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Record W4402147564 · doi:10.1177/15910199241272743

Optimizing Tigertriever adjustable stentriever technique: Operators’ experience

2024· article· en· W4402147564 on OpenAlexaff
Brian T. Jankowitz, Eitan Abergel, Ronit Agid, Abdul Rahman Al-Schameri, Krzysztof Bartosz Kadziolka, Allan Brook, Michael Diepers, Jeffrey Farkas, Johanna T Fifi, Sebastian Fischer, Chirag D. Gandhi, M. Reid Gooch, Ramesh Grandhi, Guglielmo Pero, Guy Raphaeli, Sudipta Roychowdhury, Shahram Majidi, Christian Paul Stracke, Nader Sourour, Omar Tanweer, Satoshi Tateshima, Philipp Taussky, Martin Wiesmann, Albert J. Yoo, Daniel Zumofen, Justin Singer

Bibliographic record

VenueInterventional Neuroradiology · 2024
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsToronto Western Hospital
Fundersnot available
KeywordsMedicineFLEXOperator (biology)Computer scienceTelecommunications

Abstract

fetched live from OpenAlex

The Tigertriever is a novel, radially adjustable stentriever that addresses limitations in traditional mechanical thrombectomy devices by providing enhanced user control over clot integration. This provides the ability to adapt to patient-specific factors such as varying vessel sizes and clot compositions and may be particularly crucial for ensuring efficacy and safety in distal locations. This consensus paper synthesizes the clinical techniques from a consortium of experienced international operators. It outlines the current data on the Tigertriever, discusses the new operator-controlled capabilities, and provides a recommended approach for both proximal and distal mechanical thrombectomy, emphasizing the "FLEX" approach (Fast Controlled Expansion with Relaxation) for optimal integration and reduced clot disruption.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.244
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0020.000

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 head0.025
GPT teacher head0.309
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.

Study designNot applicable
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

Citations6
Published2024
Admission routes1
Has abstractyes

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