The Utility of the Marshmallow Barium Swallow Esophagogram for Investigation of Ineffective Esophageal Motility: A Systematic and Narrative Review
Bibliographic record
Abstract
Abstract Objectives Current gold standard investigations to determine the pathology of ineffective esophageal motility (IEM) are invasive and resource-intensive. Marshmallow barium swallow esophagogram (MBSE) is emerging as a more feasible modality; however, our understanding of its role in the clinical setting is limited. Our aim was to appraise the current literature and describe the effectiveness and limitations of MBSE as a potential diagnostic tool when investigating the pathological cause of IEM. Methods A search in PubMed was conducted on May 23, 2021. Search terms included “marshmallow” AND “barium.” We included all studies which examined MBSE in the context of esophageal disease. The primary outcome of interest was to characterize the use of MBSE in current literature. Results A total of 12 studies were retrieved after initial search with 9 studies meeting final inclusion criteria. A total of 375 patients were included, with 296 patients (79%) having a relevant diagnosis or symptom prompting investigation with MBSE. The most common diagnoses included referral to a gastroenterology clinic for a barium swallow (44%), post-Angelchik insertion (23%), and dysphagia (13%). Esophageal disease was identified in both the MBSE and other screening tests in 63% participants, whereas in 27% participants abnormalities were only seen using the MBSE. Conclusion There is currently limited high-quality evidence on the use of MBSE to diagnose IEM. Further large-scale studies comparing its use in patients with different pathologic causes of IEM and of older age are required to further delineate the optimal delivery of this emerging diagnostic modality.
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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 | 0.009 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".