Esophageal Stenting for Treatment of Refractory Benign Esophageal Strictures in Dogs
Bibliographic record
Abstract
BACKGROUND: Benign esophageal strictures can recur despite multiple dilatation procedures and palliative management can be challenging. OBJECTIVE: To describe the technique and determine the outcome of esophageal stenting for treatment of refractory benign esophageal strictures (RBES) in dogs. ANIMALS: Nine dogs with RBES. METHODS: Retrospective review of records for dogs with RBES. Indwelling intraluminal esophageal stents were placed transorally with endoscopy, fluoroscopic guidance, or both. Follow-up information was obtained via medical record or telephone interview. RESULTS: Nine dogs had 10 stents placed including biodegradable stents (BDS) (6/10), self-expanding metallic stents (SEMS) (3/10), and a self-expanding plastic stent (SEPS) (1/10). All dogs had short-term improved dysphagia. Complications included ptyalism, apparent nausea, gagging, vomiting, or regurgitation (8/9), confirmed recurrence of stricture (6/9), stent migration (3/9), stent shortening (1/9), megaesophagus (1/9), incisional infection (1/9), and tracheal-esophageal fistula (1/9). Eight of 9 dogs required intervention because of the complications of which 4 of 8 dogs were eventually euthanized because of stent-related issues. One dog was lost to follow-up examination. CONCLUSIONS AND CLINICAL IMPORTANCE: Findings suggest that esophageal stent placement was safe and technically effective, but unpredictably tolerated in dogs with RBES. If a stent is placed, dogs should be monitored carefully for stent migration, dissolution of absorbable stents, and recurrence of strictures.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".