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Record W3048216925 · doi:10.7759/cureus.12071

Single-Session Treatment of Upper Extremity Deep Venous Thrombosis and Central Venous Catheter Malfunction Using the ClotTriever System

2020· article· en· W3048216925 on OpenAlexaff
Siddharth Agarwal, Christopher Sosnofsky, Jamie Saum, Manu B. Aggarwal, Sandeep Patel

Bibliographic record

VenueCureus · 2020
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsHeritage College
Fundersnot available
KeywordsMedicineThrombolysisVenous thrombosisCatheterSurgeryPulmonary embolismPercutaneousMalignancyThrombosisDeep veinInternal medicine

Abstract

fetched live from OpenAlex

Intravenous catheters account for the majority of cases of upper extremity deep vein thrombosis (UEDVT), with a higher incidence in patients suffering from malignancy. Sequelae of UEDVT are similar to that of lower extremity DVT, comprising post-thrombotic syndrome and pulmonary embolism. While there are several treatment options for UEDVT including systemic anticoagulation, catheter-directed thrombolysis, and percutaneous mechanical thrombectomy, due to the absence of consistent guidelines regarding its management, treatment is often individualized based on patient characteristics, clinical factors, and technical considerations. We present a case of a 49-year-old female suffering from breast cancer with a central venous catheter (CVC) who came to the clinic with UEDVT and CVC malfunction and was successfully treated with mechanical thrombectomy using the ClotTriever System (Inari Medical, Irvine, CA). To our knowledge, this is the first report of the ClotTriever System being used to treat UEDVT and simultaneously salvage the CVC in a single session.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.510
Threshold uncertainty score0.503

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.072
GPT teacher head0.283
Teacher spread0.211 · 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.

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

Citations9
Published2020
Admission routes1
Has abstractyes

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