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Record W4405625894 · doi:10.1002/ncp.11263

Long‐term enteral nutrition with a nasogastric tube can be safe and effective: A case report

2024· article· en· W4405625894 on OpenAlexaff
James Duerksen, Bram Ramjiawan, Donald R. Duerksen

Bibliographic record

VenueNutrition in Clinical Practice · 2024
Typearticle
Languageen
FieldNursing
TopicClinical Nutrition and Gastroenterology
Canadian institutionsUniversity of ManitobaSt. Boniface Hospital
Fundersnot available
KeywordsMedicineParenteral nutritionTerm (time)Tube (container)Enteral administrationIntensive care medicineFeeding tubeSurgeryWaste management

Abstract

fetched live from OpenAlex

Home enteral nutrition (HEN) is a vital feeding practice for those who have chronic disorders that prevent them from eating normally. Although short-term feeding is predominantly done via nasogastric (NG) tubes and long-term feeding is done via percutaneous endoscopic gastrostomy (PEG) tube, we present a case that demonstrates that the long-term use of NG tubes may be possible. Our case involves an adult woman who has been fed via an NG tube for >3 years with no complications. She has had three replacement tubes inserted over these 3 years and has not required any healthcare visits related to tube dysfunction or complications. She continues to do well. A literature search determined that there are no reports of long-term use (greater than a year) of NG feeding tubes in outpatient adults, and thus the true rate of complications related to NG tubes is unknown. We review the reported complications associated with long-term PEG tubes. Although PEGs are typically regarded as safer in long-term feeding situations, this case demonstrates that NG tubes could prove effective under certain circumstances in which the insertion of a PEG may not be possible.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.003
Science and technology studies0.0040.003
Scholarly communication0.0030.005
Open science0.0020.003
Research integrity0.0100.006
Insufficient payload (model declined to judge)0.0030.002

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 head0.038
GPT teacher head0.418
Teacher spread0.380 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations1
Published2024
Admission routes1
Has abstractyes

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