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Record W2137512486 · doi:10.1111/sdi.12055

Education in Vascular Access

2013· article· en· W2137512486 on OpenAlexaff
Louise Moist, Timmy C. Lee, Charmaine E. Lok, Ahmed A. Al‐Jaishi, Xi Wang, V. Campbell, Janet Graham, Barb Wilson, Tushar J. Vachharajani

Bibliographic record

VenueSeminars in Dialysis · 2013
Typearticle
Languageen
FieldHealth Professions
TopicCentral Venous Catheters and Hemodialysis
Canadian institutionsOttawa HospitalSt. Michael's HospitalToronto General HospitalUniversity of TorontoWestern University
Fundersnot available
KeywordsMedicineMultidisciplinary approachVascular accessDysfunctional familyExperiential learningPatient careNursingMedical educationHemodialysisSurgeryPedagogyPsychology

Abstract

fetched live from OpenAlex

The successful creation and use of an arteriovenous vascular access (VA) requires a coordinated, educated multidisciplinary team to ensure an optimal VA for each patient. Patient education programs on VA are associated with increased arteriovenous VA use at dialysis initiation. Education should be tailored to patient goals and preferences with the understanding that experiential education from patient to patient is far more influential than that provided by the healthcare professional. VA education for the nephrologist should focus on addressing the systematic and patient-level barriers in achieving a functional VA, with specific components relating to VA creation, maturation, and cannulation that consider patient goals and preferences. A deficit in nursing skills in the area of assessment and cannulation can have devastating consequences for hemodialysis patients. Delivery of an integrated education program increases nurses' knowledge of VA and development of simulation programs or constructs to assist in cannulation of the VA will greatly facilitate the much needed skill transfer. Adequate VA surgical training and experience are critical to the creation and outcomes of VA. Simulations can benefit nephrologists, dialysis nurses surgeons, and interventionalists though aiding in surgical creation, understanding of the physiology and anatomy of a dysfunctional VA, and practicing cannulation techniques. All future educational initiatives must emphasize the importance of multidisciplinary care to attain successful VA outcomes.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.024
GPT teacher head0.374
Teacher spread0.350 · 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

Citations29
Published2013
Admission routes1
Has abstractyes

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