Esophagogastric junction outflow obstruction on manometry: Outcomes and lack of benefit from CT and EUS
Bibliographic record
Abstract
BACKGROUND: Esophagogastric junction outflow obstruction (EGJOO) is a manometric diagnosis based on the Chicago Classification defined by inadequate relaxation of the gastroesophageal junction (GEJ) with swallowing, but with sufficient peristalsis such that the criteria for achalasia are not met. Possible causes include anatomical and functional etiologies. Further investigations, including computed tomography (CT) of the chest and endoscopic ultrasound (EUS), to help elucidate the etiology of EGJOO have been suggested, but the utility of this approach has not been proven. METHODS: All new diagnoses of EGJOO made in the calendar years 2015-2016 were included. A review was performed for each patient to assess clinical outcomes, diagnostic, and therapeutic interventions after the EGJOO diagnosis. KEY RESULTS: 107 EGJOO patients were included. Their primary complaints were dysphagia (68%), chest pain (12%), reflux (8%), pre-operative assessment (6%), regurgitation (3%), and cough (3%). The mean IRP was 21.8 mm Hg. After a mean follow-up period of 463 days, the etiology of EGJOO remained undetermined in 67% of patients. 48% of patients were investigated with cross-sectional imaging (and 10% with EUS to rule out external compression or malignancy as a cause of EGJOO; none of these tests provided any further useful information). In only two cases did the EGJOO progress to achalasia. CONCLUSIONS & INFERENCES: EGJOO is a manometric diagnosis with unclear clinical significance and outcome. CT and EUS of the GEJ were unhelpful at determining the cause of this entity. In this series, very few appear to progress to achalasia, none were due to malignancy, and many resolved spontaneously.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".