MétaCan
Menu
Back to cohort

Sublingual Nitroglycerine for Esophageal Meat Impaction

2009· article· en· W2979279039 on OpenAlexaff
Mohammad Taheri‬, Alexandra Ilnyckyj, Wayne Manishen

Bibliographic record

VenueThe American Journal of Gastroenterology · 2009
Typearticle
Languageen
FieldMedicine
TopicForeign Body Medical Cases
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineDysphagiaEosinophilic esophagitisHeartburnSwallowingEmergency departmentGERDEsophagusAnesthesiaEsophageal diseaseSurgeryInternal medicineRefluxDisease

Abstract

fetched live from OpenAlex

Purpose: A 48 year old female presented to a community hospital emergency room with sudden onset of dysphagia to liquids and solids 3 hours after eating meat for supper. She denied heartburn, tobacco or alcohol use and had no previous problems with swallowing. She was not taking any medications and was unable to tolerate saliva. A trial of glucagon resulted in no improvement. The emergency room physician contacted the endoscopist on-call at the teaching hospital who suggested that she be given a trial of sublingual nitroglycerine 0.3 mg. This agent was suggested as it has been shown to cause a significant reduction in lower esophageal sphincter pressure in normal subjects as well as in patients with achalasia, Chagas' disease and esophageal spasm (1-4). Within 20 minutes after sublingual nitroglycerine adminstration, the patient passed the meat bolus spontaneously. She did develop mild transient hypotension from the nitroglycerine which responded to intravenous fluids. Follow-up elective endoscopy 2 months later revealed a Schatzki ring which was dilated with a 48 French Maloney dilator. Biopsies of the esophagus showed only mild non-secific inflammation, without evidence of eosinophilic esophagitis. Based on this experience, it appears that sublingual nitroglycerine may be helpful in emergency room management of esophageal meat impaction and may reduce the need for urgent endoscopy.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.624
Threshold uncertainty score0.307

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 head0.014
GPT teacher head0.309
Teacher spread0.294 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueThe American Journal of GastroenterologySame topicForeign Body Medical CasesFrench-language works237,207