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Record W4386849508 · doi:10.26550/2209-1092.1271

Peripheral nerve catheter securement: A narrative literature review

2023· article· en· W4386849508 on OpenAlexaboutno aff
Joshua M Wiesel, Bernadette R Findlay, Li Ching Ooi, Jennifer Stevens, Renata Hadzic

Bibliographic record

VenueJournal of Perioperative Nursing · 2023
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePeripheral nerveCatheterPeripheralNarrative reviewSurgeryDisplacement (psychology)AnesthesiaIntensive care medicineInternal medicinePsychologyAnatomy

Abstract

fetched live from OpenAlex

Peripheral nerve catheters are commonly used to provide analgaesia and improve patient outcomes. Catheter dislodgment, displacement or leakage can result in premature cessation of analgaesic effect. There are currently no published guidelines for how to secure peripheral nerve catheters. This narrative review explores and integrates the available research into the efficacy of peripheral nerve catheter securement products and techniques to reduce catheter dislodgement and displacement. All studies looking at peripheral nerve catheter securement methods were included from inception until 19 October 2022 across PUBMED, Scopus, Ovid, Google Scholar, EMBASE and The Cochrane Library. The Jadad scale and Newcastle–Ottawa scale were used to assess the methodological quality of randomised controlled trials and observational studies, respectively. Sixteen papers were included in this review. The results were mixed and substantial heterogeneity across studies further limited the ability to draw firm or generalisable conclusions. Rather, several products and techniques that may reduce catheter dislodgement, displacement or leakage, that can contribute to dislodgement, were identified for further investigation. There was some evidence to support the use of the catheter over needle technique, adhesive dressings and tissue adhesives. The number of studies investigatingsubcutaneous tunnelling and anchoring devices was particularly limited.

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)0.000
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 score0.594
Threshold uncertainty score0.529

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.022
GPT teacher head0.326
Teacher spread0.303 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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