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A Patient‐Focused Approach to Thrombolytic Use in the Management of Catheter Malfunction

2006· article· en· W1929256014 on OpenAlexaff
Charmaine E. Lok, Alison Thomas, Lavern M. Vercaigne

Bibliographic record

VenueSeminars in Dialysis · 2006
Typearticle
Languageen
FieldHealth Professions
TopicCentral Venous Catheters and Hemodialysis
Canadian institutionsUniversity of ManitobaSt. Michael's HospitalToronto General HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineCatheterThrombusContext (archaeology)Intensive care medicineThrombolytic drugDialysisPresentation (obstetrics)Medical emergencySurgeryThrombolysisInternal medicineMyocardial infarction

Abstract

fetched live from OpenAlex

ABSTRACT Thrombus‐related catheter malfunction is a significant problem for catheter‐dependent dialysis patients. The primary medical intervention is the local luminal installation of thrombolytic agents (TLAs). There are three major TLA installation methodologies: locking, push, and infusion protocols. A systematic literature review of existing TLA protocols for treating dialysis catheter malfunction was performed using the PubMed and EMBASE (Drugs and Pharmacology) databases from the time of each database's inception to August 2005. Thrombolytic administration was then categorized according to the patient's clinical need: (1) an acute/immediate requirement, such as when malfunction prohibits dialysis initiation, and (2) rescue therapy, such as when the thrombus threatens to significantly impair current or subsequent dialysis clearance. Published TLA protocols are discussed in the context of their clinical requirement (acute or rescue therapy). A unifying clinically relevant management algorithm that considers the etiology of catheter malfunction as being thrombus related or not, and an approach to TLA use based on clinical presentation is described. This algorithm was developed after a systematic review of the literature. The application of this treatment algorithm requires prospective validation and study.

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)0.000
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 score0.079
Threshold uncertainty score0.540

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.031
GPT teacher head0.298
Teacher spread0.267 · 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

Citations15
Published2006
Admission routes1
Has abstractyes

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