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Record W3024141090 · doi:10.1055/s-0040-1709153

Evolution of Stroke Thrombectomy Techniques to Optimize First-Pass Complete Reperfusion

2020· review· en· W3024141090 on OpenAlexaff
Johanna M. Ospel, Ryan McTaggart, Nima Kashani, Marios Psychogios, Mohammed Almekhlafi, Mayank Goyal

Bibliographic record

VenueSeminars in Interventional Radiology · 2020
Typereview
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineOcclusionStenosisStroke (engine)CatheterBalloonStandard of careComplicationSurgeryRadiologyIntensive care medicine

Abstract

fetched live from OpenAlex

Since 2015, endovascular therapy (EVT) has become the standard of care for acute ischemic stroke (AIS) due to large vessel occlusion. It is a safe and highly effective treatment, and its number needed to treat of 2.6 is one of the highest throughout medicine. The ultimate goal when performing EVT is to maximize chances of good outcome through achievement of fast first-pass complete reperfusion, as incomplete and delayed reperfusion increases complication rates and negatively affects outcome. Since EVT has been established as standard of care, new devices have been developed and treatment techniques have been refined. This review provides a brief overview about the rationale for and history of EVT, followed by a detailed step-by-step description of how to perform EVT using the BADDASS (BAlloon guide with large bore Distal access catheter with Dual Aspiration with Stent-retriever as Standard approach), a combined technique, which is in our opinion the safest and most effective way to achieve fast first-pass complete reperfusion. We also discuss treatment strategies for patients with simultaneous high-grade carotid stenosis/pseudoocclusion/occlusion and gaining carotid access in challenging arch anatomy, as these are commonly encountered situations in AIS, and conclude with an outlook on new technologies and future directions of EVT.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.688
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.035
GPT teacher head0.340
Teacher spread0.305 · 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
GenreReview

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

Citations25
Published2020
Admission routes1
Has abstractyes

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