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Record W3207404731 · doi:10.29390/cjrt-2021-026

Evaluation of a novel endotracheal tube suctioning system incorporating an inflatable sweeper

2021· article· en· W3207404731 on OpenAlexvenueno aff
John Dwyfor Davies, Yuh Chin Huang, Neil R. MacIntyre

Bibliographic record

VenueCanadian Journal of Respiratory Therapy · 2021
Typearticle
Languageen
FieldMedicine
TopicNosocomial Infections in ICU
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSuctionCatheterEndotracheal tubeCrossover studyLumen (anatomy)AirwayMechanical ventilationTube (container)AnesthesiaSurgeryIntubationMaterials sciencePathology

Abstract

fetched live from OpenAlex

INTRODUCTION: Accumulation of secretions in an endotracheal tube can increase the resistance to flow resulting in an increased patient work of breathing when the patient is interacting with the ventilator. Retained secretions can also serve as an infection risk. Standard suction catheters are limited in their ability to keep the lumen of the endotracheal tube clear. A novel closed-suction catheter has been introduced that incorporates a balloon at its distal end that, when inflated, physically scrapes secretions out of the endotracheal tube (CleanSweep catheter (CSC), Teleflex, Morrisville NC). We hypothesized that the CSC would be more efficient at removing secretions from inside the endotracheal tube than a standard suction catheter (SSC). METHODS: We performed a bench study examining resistive pressures across different sizes of endotracheal tubes when cleaned by the CSC as compared with an SSC. This study was followed by a prospective crossover study again comparing the CSC with an SSC in intubated intensive care unit patients receiving mechanical ventilation and requiring frequent suctioning. RESULTS: < 0.05). CONCLUSION: Both our bench and crossover clinical study demonstrated improved clearance of secretions with the CSC vs an SSC. Further research is needed to ascertain the clinical outcome benefits of enhanced secretion removal.

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)0.003
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.118
GPT teacher head0.346
Teacher spread0.228 · 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 designBench or experimental
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

Citations3
Published2021
Admission routes1
Has abstractyes

Explore more

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