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Record W4306784442 · doi:10.14740/gr1533

Safety of Percutaneous Endoscopic Gastrostomy Placement in Patients With SARS-CoV-2 Infection

2022· article· en· W4306784442 on OpenAlexvenueno aff
Ayushi Shah, Zunirah Ahmed, Fadl A. Zeineddine, Eamonn M.M. Quigley

Bibliographic record

VenueGastroenterology Research · 2022
Typearticle
Languageen
FieldNursing
TopicClinical Nutrition and Gastroenterology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePercutaneous endoscopic gastrostomyCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakPercutaneousGastrostomySurgeryVirologyInternal medicine

Abstract

fetched live from OpenAlex

Background: Coronavirus disease 2019 (COVID-19) can lead to ventilator-dependent chronic respiratory failure and a need for tube feeding. Percutaneous endoscopic gastrostomy (PEG) placement provides more sustainable longer-term enteral access with fewer side effects compared to the long-term nasogastric tube placement. Bleeding is a recognized complication of PEG placement, and many COVID-19 patients are on antiplatelets/anticoagulants, yet minimal data exist on the safety of PEG tube placement in this context. Methods: A retrospective chart review identified patients who underwent PEG placement between January 2020 and January 2021 at a single institution. Success was defined as PEG placement and use to provide enteral nutrition with no complications requiring removal within 4 weeks. Results: Thirty-six patients with and 104 age- and sex-matched patients without COVID-19 infection were included. More COVID-19 patients were obese, on anticoagulants, had low serum albumin levels and had a tracheostomy in place. Of those patients, 8.3% with COVID-19 developed PEG-related complications compared to 16.3% without (P = 0.28). PEG success rates in patients with and without COVID-19 were similar at 97.2% and 92.3%, respectively (P = 0.44). Conclusion: PEG tube placement is comparatively safe in COVID-19 patients who need long-term enteral access.

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)0.001
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 score0.023
Threshold uncertainty score0.736

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.039
GPT teacher head0.349
Teacher spread0.311 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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