Sublingual Nitroglycerine for Esophageal Meat Impaction
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".