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Record W2981431127 · doi:10.12968/bjon.2019.28.19.s22

Cyanoacrylate tissue adhesive: a new tool for the vascular access toolbox

2019· article· en· W2981431127 on OpenAlexaff
Jackie Nicholson, Jocelyn Hill

Bibliographic record

VenueBritish Journal of Nursing · 2019
Typearticle
Languageen
FieldHealth Professions
TopicCentral Venous Catheters and Hemodialysis
Canadian institutionsProvidence Health Care
Fundersnot available
KeywordsCyanoacrylateToolboxVascular accessHealth professionalsVascular graftMedicineHealth careClinical PracticeNursingBusinessAdhesiveSurgeryEngineeringPolitical scienceMechanical engineering

Abstract

fetched live from OpenAlex

Health professionals are responsible for preventing and minimising complications related to vascular access devices. This is important from the perspectives of both the patient and the health economy. Practitioners have many tools at their disposal and evidence is available to assist in using these tools to enhance best practice. A relatively new tool has been acknowledged as having a role in vascular access as well as previously recognised roles in other areas of healthcare. Cyanoacrylate tissue adhesive has been approved for use with vascular access devices and the benefits of this aid to device securement are now being recognised.

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)0.007
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0010.004
Scholarly communication0.0050.007
Open science0.0010.003
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0150.007

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 head0.057
GPT teacher head0.405
Teacher spread0.348 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations25
Published2019
Admission routes1
Has abstractyes

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