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Record W2139573109 · doi:10.1111/sdi.12046

Understanding Surgical Preference and Practice in Hemodialysis Vascular Access Creation

2013· article· en· W2139573109 on OpenAlexaffabout
Andra Nica, Charmaine E. Lok, Jeremy P. Harris, Timmy C. Lee, Michele H. Mokrzycki, Ivan D. Maya, Miguel A. Vazquez, Xi Wang, Louise Moist

Bibliographic record

VenueSeminars in Dialysis · 2013
Typearticle
Languageen
FieldHealth Professions
TopicCentral Venous Catheters and Hemodialysis
Canadian institutionsToronto General HospitalWestern University
Fundersnot available
KeywordsMedicineVascular accessPreferenceHemodialysisIntensive care medicineSurgery

Abstract

fetched live from OpenAlex

Understanding healthcare providers' preferences, values, and beliefs around AVF eligibility is important to explain variability in practice. We conducted a survey of international surgeons, using hypothetical patient scenarios, to assess resources used, variables, perceived barriers, and absolute contraindications to access creation. A total of 134 surgeons completed the survey. Venous duplex ultrasound mapping (VDUM) was offered to all patients by 90% of US, 68% Canadian, and 63% European respondents. VDUM altered clinical decision less than 25% of the time for 33% American, 48% Canadian, and 85% European surgeons. Increased comorbidities and previous failed access were deterrents to AVF creation as was vessel size. Second choice access was the AV graft in the US and Europe and the catheter in Canada. Absolute contraindications to AVF creation included patient life expectancy <1 year, left ventricular ejection fraction (LVEF) <15%, and a history of dementia, while 42% surgeons reported no absolute contraindications. Perceived barriers included patient preferences, long wait times for surgery, and late referral to a Nephrologist. Significant variability exists in the surgical preoperative assessment of patients, and the eligibility criteria used for fistula creation. Understanding surgeons' preferences can aid in establishing standardization for VA access eligibility, including surgical assessment.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.255
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.134
GPT teacher head0.394
Teacher spread0.260 · 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

Citations21
Published2013
Admission routes2
Has abstractyes

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