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Record W2328543679 · doi:10.1097/imi.0b013e3181f8b79d

Peripheral Insertion Techniques for the Impella 5.0 Circulatory Support System

2010· article· en· W2328543679 on OpenAlexaff
Jamil Bashir, Matt Klas, Anson Cheung

Bibliographic record

VenueInnovations Technology and Techniques in Cardiothoracic and Vascular Surgery · 2010
Typearticle
Languageen
FieldEngineering
TopicMechanical Circulatory Support Devices
Canadian institutionsSt. Paul's Hospital
FundersAbiomed
KeywordsImpellaPeripheralCirculatory systemBrachial arteryInsertion lossMedicineBiomedical engineeringComputer scienceVentricular assist deviceCardiologyEngineeringInternal medicineElectrical engineeringHeart failureBlood pressure

Abstract

fetched live from OpenAlex

OBJECTIVE: The Impella 5.0 is a peripherally inserted short-term left ventricular assist device. It is typically inserted through a large peripheral artery such as the common femoral artery. We describe both a graft technique and a purse-string technique in detail. METHODS: In the course of introducing this technology to our hospital, we independently developed two techniques for the insertion of the device. These novel insertion techniques are different from those described by the manufacturer. RESULTS: The techniques described have distinct advantages that may be helpful to physicians inserting the device. Common areas of difficulty with insertion of the device and solutions to these problems are also described. CONCLUSIONS: Insertion techniques for the Impella 5.0 are described in detail.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.012
GPT teacher head0.250
Teacher spread0.239 · 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 designNot applicable
Domainnot available
GenreMethods

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
Published2010
Admission routes1
Has abstractyes

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