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Record W2138975123 · doi:10.1093/ageing/afq088

Does looped nasogastric tube feeding improve nutritional delivery for patients with dysphagia after acute stroke? A randomised controlled trial

2010· article· en· W2138975123 on OpenAlexaff
J. Beavan, Simon Conroy, Rowan Harwood, John Gladman, Jo Leonardi‐Bee, Tracey Sach, T.E. Bowling, W Sunman, Catherine Gaynor

Bibliographic record

VenueAge and Ageing · 2010
Typearticle
Languageen
FieldNursing
TopicClinical Nutrition and Gastroenterology
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineDysphagiaStroke (engine)Randomized controlled trialTolerabilityConfidence intervalFeeding tubeAcute strokeAdverse effectSurgeryPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: nasogastric tube (NGT) feeding is commonly used after stroke, but its effectiveness is limited by frequent dislodgement. OBJECTIVE: the objective of the study was to evaluate looped NGT feeding in acute stroke patients with dysphagia. METHODS: this was a randomised controlled trial of 104 patients with acute stroke fed by NGT in three UK stroke units. NGT was secured using either a nasal loop (n = 51) or a conventional adhesive dressing (n = 53). The main outcome measure was the proportion of prescribed feed and fluids delivered via NGT in 2 weeks post-randomisation. Secondary outcomes were frequency of NGT insertions, treatment failure, tolerability, adverse events and costs at 2 weeks; mortality; length of hospital stay; residential status; and Barthel Index at 3 months. RESULTS: participants assigned to looped NGT feeding received a mean 17% (95% confidence interval 5-28%) more volume of feed and fluids, required fewer NGTs (median 1 vs 4), and had fewer electrolyte abnormalities than controls. There was more minor nasal trauma in the loop group. There were no differences in outcomes at 3 months. Looped NGT feeding cost 88 pounds sterling more per patient over 2 weeks than controls. CONCLUSION: looped NGT feeding improves delivery of feed and fluids and reduces NGT reinsertion with little additional cost.

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: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.006
GPT teacher head0.240
Teacher spread0.234 · 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 designRandomized trial
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

Citations56
Published2010
Admission routes1
Has abstractyes

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