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Physical examination: How to examine the arm with arteriovenous fistula

2012· review· en· W1503852154 on OpenAlexvenueno aff
Clemente Neves Sousa, João Apóstolo, María Henriqueta Figueiredo, María Manuela Martins, Vanessa Filipa Ferreira Dias

Bibliographic record

VenueHemodialysis International · 2012
Typereview
Languageen
FieldHealth Professions
TopicCentral Venous Catheters and Hemodialysis
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePhysical examinationArteriovenous fistulaHemodialysisStenosisFistulaSurgeryRadiologyIntensive care medicine

Abstract

fetched live from OpenAlex

Physical examination has demonstrated its effectiveness in identifying complications of arteriovenous fistula (AVF). It should be initiated at the stage prior to the construction of the AVF and continue in its accomplishment, maturation, and subsequent use in the treatment of hemodialysis. Nurses should incorporate the physical examination in their practices, in order to preserve the vascular net of patients and assist in the recognition of complications of AVF. It is intended to describe aspects of the physical examination that enable the identification of the AVF complications including: infection, accessory veins, venous stenosis, steal syndrome, high-output cardiac failure, and venous hypertension.

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)0.004
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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0010.001
Scholarly communication0.0010.004
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0060.005

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 head0.082
GPT teacher head0.377
Teacher spread0.296 · 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

Citations36
Published2012
Admission routes1
Has abstractyes

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