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Record W2145308179 · doi:10.14740/gr593w

Combined Placement of Covered Self-Expanding Metallic Stents and Nasojejunal Tube for Managing Large Lower Esophageal Perforations

2014· article· en· W2145308179 on OpenAlexvenueno aff
Surinder Singh Rana

Bibliographic record

VenueGastroenterology Research · 2014
Typearticle
Languageen
FieldMedicine
TopicEsophageal and GI Pathology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTube (container)PerforationNanotechnologyMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Covered self-expanding metallic stents (cSEMSs) have emerged as effective treatment option for esophageal perforations. However, the large lower esophageal perforations where the cSEMS is placed across gastroesophageal junction have lower healing rates because refluxed gastric contents constantly irritate perforation and also there is increased risk of stent migration. Moreover, gastric mucosa tends to prolapse into lumen of lower end of stent causing its obstruction, leading to seepage of saliva and fluids from upper end of stent even in the patients who are on parenteral nutrition. We present our experience of a novel technique of combined cSEMS and nasojejunal tube (NJT) placement in four patients (two males) with benign large lower esophageal perforations. The NJT was placed through the stent into the jejunum through which patients were given enteral feeding. The stents were placed 5 - 21 days after esophageal perforation with the size of perforation ranging from 4 to 6 cm. As the NJT formed a loop in stomach, it prevented migration of stent. And also its presence in lumen of stent prevented its obstruction by prolapsing gastric mucosa, thereby preventing seepage of saliva and fluids from side of stent. Both stents and NJT were removed after 6 weeks and leak closed in all patients. Combined cSEMS and NJT placement seems to be safe and effective for treating large lower esophageal perforations. NJT placement seems to decrease risk of migration, prevents seepage of fluids and permits early enteral nutrition, thereby improving the healing rates.

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)0.002
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 score0.093
Threshold uncertainty score0.509

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.034
GPT teacher head0.350
Teacher spread0.316 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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