Combined Placement of Covered Self-Expanding Metallic Stents and Nasojejunal Tube for Managing Large Lower Esophageal Perforations
Bibliographic record
Abstract
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.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".