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Prevention of Gastroesophageal Reflux Using an Application of Half‐Solid Nutrients in Patients with Percutaneous Endoscopic Gastrostomy Feeding

2004· letter· en· W2148421704 on OpenAlexfundno aff
Jiro Kanie, Yusuke Suzuki, Akihisa Iguchi, Hiroyasu Akatsu, Takayuki Yamamoto, Hiroshi Shimokata

Bibliographic record

VenueJournal of the American Geriatrics Society · 2004
Typeletter
Languageen
FieldNursing
TopicClinical Nutrition and Gastroenterology
Canadian institutionsnot available
FundersUniversity of TorontoInstitute for Clinical Evaluative Sciences
KeywordsMedicineHounsfield scaleNutrientPercutaneous endoscopic gastrostomyRefluxBolus (digestion)VomitingParenteral nutritionEnteral administrationEsophagusPEG ratioSurgeryNuclear medicineInternal medicineComputed tomography

Abstract

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To the Editor: Although percutaneous endoscopic gastrostomy (PEG) feeding is widely used as a convenient method of long-term nutritional support,1 administration of liquid nutrients often accompanies complications such as vomiting or diarrhea. Gastroesophageal reflux (GER) presumably causes vomiting, which may result in aspiration. Therefore, half-solid nutrients were used for PEG feeding, and whether this approach can reduce GER was examined. Seventeen patients (mean age±standard deviation=79.9±10.5) who were on PEG feeding participated in this study. Written informed consent was obtained from all patients. Liquid or half-solid nutrients were administered via PEG tubing in a randomized order. Half-solid nutrients were prepared by mixing 5 g of agarose with 500 mL of liquid nutrients diluted with the same volume of water. Incidence of GER was assessed using computed tomography (CT) scan of the esophagus. Liquid nutrients were administered over 15 minutes in portions of 400 mL containing 20 mL of the water-soluble contrast material, Gastrografin® (methylglucamine diatrizoate). The half-solid nutrients were administered via bolus injections of the same volume of nutrients, which were contained separately in 50 mL syringes. Thirty minutes after the administration, a CT scan was performed in 1-cm-thick slices of the esophageal portion. GER was confirmed if the Hounsfield number exceeded 100 in each slice examined. A Hounsfield number of 100 was employed because it can unequivocally distinguish the mixture of the nutrients containing contrast material from the esophageal and other surrounding tissues. A radiologist who was not informed of the type of nutrients used assessed the CT images. Statistical comparison of the incidence of GER between the two types of nutrients was made using McNemar test. GER was confirmed in 10 of the 17 subjects (58.8%) when they received liquid nutrients. By contrast, when they received half-solid nutrients, only four of 17 subjects (23.5%) showed evidence of GER from CT findings (χ2=6.0, df=1, P=.014, by McNemar test) (Table 1). The advantages of PEG feeding over nasogastric feeding have been discussed elsewhere, although there have been some complications reported.2 Of the complications, vomiting can be a cause of fatal aspiration due to a reflux of the administered nutrients.3 The tubing used for PEG feeding has made it possible to apply half-solidified nutrients, which we hypothesized would cause less reflux from the stomach.4 As expected, less evidence of GER was observed when using half-solid nutrients than when using liquid nutrients. It was also confirmed that solidifying nutrients using agarose did not clog the tube. Continuous infusion and careful observation of the patient's symptoms are considered necessary to reduce the risk of GER in PEG feeding. Also, the patients are advised to remain in a sitting position during administration, which may increase the risk of developing or exacerbating decubitus ulcers. Thus, this pilot study suggests that the use of rapid administration of half-solid nutrients in PEG feeding can reduce the risk of GER substantially and may eventually contribute to a reduction of complications and to improvement in the quality of life of patients and their caregivers.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.013
GPT teacher head0.292
Teacher spread0.279 · 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

Citations45
Published2004
Admission routes1
Has abstractyes

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