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Record W1980815385 · doi:10.3138/physio.61.2.107

Consensus among Physiotherapists in the United Kingdom on the Use of Normal Saline Instillation Prior to Endotracheal Suction: A Delphi Study

2009· article· en· W1980815385 on OpenAlexvenueno aff
Fiona Roberts

Bibliographic record

VenuePhysiotherapy Canada · 2009
Typearticle
Languageen
FieldMedicine
TopicNosocomial Infections in ICU
Canadian institutionsnot available
Fundersnot available
KeywordsSalineMedicineSuctionDelphiDelphi methodHypertonic salinePhysical therapyAnesthesiaComputer science

Abstract

fetched live from OpenAlex

PURPOSE: To investigate whether physiotherapists consider normal saline instillation (NSI) useful before endotracheal suctioning and, if so, when. METHODS: Sixty-eight respiratory physiotherapists recruited from the United Kingdom's Association of Chartered Physiotherapists in Respiratory Care agreed to participate in a Delphi study. Clinicians' opinion of when NSI should be used was established and developed into statements. Level of agreement with each statement was collated through three rounds of a questionnaire. Clinicians' experiences were also reported. RESULTS: From the 52 responses to the third questionnaire, there was consensus that respiratory physiotherapists in the United Kingdom would use NSI when sputum retention is a problem, particularly when treatment options are limited (96%) and when sputum is obstructing the airway (92%). They agreed that non-bronchoscopic bronchoalveolar lavage can be used to resolve persistent atelectasis (70%). They would not use NSI for a test treatment during initial assessment without evidence of retained secretions (94%), when secretions are copious but can be cleared by alternative physiotherapy techniques (96%), to enhance a cough unless very strong evidence indicates retained secretions (81%), or to compensate for inadequate suction technique (90%). CONCLUSION: This study provides clinicians' views about when NSI could be used.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.773
Threshold uncertainty score0.602

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.076
GPT teacher head0.342
Teacher spread0.266 · 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 designObservational
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

Citations16
Published2009
Admission routes1
Has abstractyes

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