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Record W2800754039 · doi:10.1111/sdi.12708

Self‐cannulation of the vascular access in home hemodialysis: Overcoming patient‐level barriers

2018· review· en· W2800754039 on OpenAlexaff
Frank L. Ward, Rose Faratro, Rory McQuillan

Bibliographic record

VenueSeminars in Dialysis · 2018
Typereview
Languageen
FieldHealth Professions
TopicCentral Venous Catheters and Hemodialysis
Canadian institutionsToronto General HospitalUniversity Health Network
Fundersnot available
KeywordsMedicineHome hemodialysisHemodialysisHealth careIdentification (biology)Vascular accessIntensive care medicineMedical emergencyHealthcare systemNursingSurgery

Abstract

fetched live from OpenAlex

Patients with end-stage kidney disease who are considering home hemodialysis (HHD) face the challenge of learning to self-cannulate their arteriovenous access. Current practice discourages the use of tunneled central venous catheters, with recent indications that self-cannulating patients have superior outcomes. Patient-level barriers do not appear to preclude a successful HHD program and should not be viewed as insurmountable by healthcare staff or patients. The healthcare team must address patient fears while instructing the patient to perform self-cannulation safely. Identification and understanding of the barriers perceived by the patient will allow the patient and healthcare team work collaboratively, toward the goal of independence with self-cannulation both during initial training and follow-up care. The aim of this review was to provide a practical resource to aid in the identification and resolution of these patient-level barriers, with the overall goal of improving the patient experience and medical outcomes in home hemodialysis programs.

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)0.001
metaresearch head score (Gemma)0.005
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: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.058
GPT teacher head0.377
Teacher spread0.319 · 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
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

Citations16
Published2018
Admission routes1
Has abstractyes

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