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Record W2046635129 · doi:10.1155/2012/170857

Minimizing Hemodialysis Catheter Dysfunction: An Ounce of Prevention

2012· article· en· W2046635129 on OpenAlexaff
Timmy Lee, Charmaine E. Lok, Miguel A. Vazquez, Louise Moist, Ivan D. Maya, Michele H. Mokrzycki

Bibliographic record

VenueInternational Journal of Nephrology · 2012
Typearticle
Languageen
FieldHealth Professions
TopicCentral Venous Catheters and Hemodialysis
Canadian institutionsWestern UniversityToronto General HospitalUniversity of TorontoUniversity Health Network
FundersNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsMedicineHeparinCatheterHemodialysisIntensive care medicineRegimenSurgery

Abstract

fetched live from OpenAlex

The maintenance of tunneled catheter (TC) patency is critical for the provision of adequate hemodialysis in patients who are TC-dependent. TC dysfunction results in the need for costly and inconvenient interventions, and reduced quality of life. Since the introduction of TCs in the late 1980s, heparin catheter lock has been the standard prophylactic regimen for the prevention of TC dysfunction. More recently, alternative catheter locking agents have emerged, and in some cases have shown to be superior to heparin lock with respect to improving TC patency and reducing TC-associated infections. These include citrate, tissue plasminogen activator, and a novel agent containing sodium citrate, methylene blue, methylparaben, and propylparaben. In addition, prophylaxis using oral anticoagulants/antiplatelet agents, including warfarin, aspirin, ticlodipine, as well as the use of modified heparin-coated catheters have also been studied for the prevention of TC dysfunction with variable results. The use of oral anticoagulants and/or antiplatelet agents as primary or secondary prevention of TC dysfunction must be weighed against their potential adverse effects, and should be individualized for each patient.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.449
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.059
GPT teacher head0.390
Teacher spread0.332 · 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 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

Citations27
Published2012
Admission routes1
Has abstractyes

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