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Record W2524442850 · doi:10.1177/2054358116669125

Arteriovenous Vascular Access Selection and Evaluation

2016· article· en· W2524442850 on OpenAlexafffund
Jennifer M. MacRae, Matthew J. Oliver, Edward G. Clark, Christine Dipchand, Swapnil Hiremath, Joanne Kappel, Mercedeh Kiaii, Charmaine E. Lok, Rick Luscombe, Lisa Miller, Louise Moist

Bibliographic record

VenueCanadian Journal of Kidney Health and Disease · 2016
Typearticle
Languageen
FieldHealth Professions
TopicCentral Venous Catheters and Hemodialysis
Canadian institutionsUniversity of ManitobaProvidence Health CareUniversity of British ColumbiaUniversity of SaskatchewanWestern UniversityUniversity of TorontoHealth Sciences CentreUniversity Health NetworkDalhousie UniversityUniversity of OttawaSunnybrook Health Science CentreUniversity of Calgary
FundersCanadian Society of Nephrology
KeywordsMedicineVascular accessSelection (genetic algorithm)Intensive care medicineInternal medicineHemodialysisComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

When making decisions regarding vascular access creation, the clinician and vascular access team must evaluate each patient individually with consideration of life expectancy, timelines for dialysis start, risks and benefits of access creation, referral wait times, as well as the risk for access complications. The role of the multidisciplinary team in facilitating access choice is reviewed, as well as the clinical evaluation of the 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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.398
Threshold uncertainty score0.954

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.045
GPT teacher head0.371
Teacher spread0.326 · 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 designNot applicable
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

Citations43
Published2016
Admission routes2
Has abstractyes

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