Randomized controlled trial to investigate the effect of metal clips on early migration during stent implantation for malignant esophageal stricture
Bibliographic record
Abstract
BACKGROUND: The rate of stent migration, especially in the short term after implantation, is high in the treatment process. We sought to explore an effective method for preventing early migration after stent implantation for malignant esophageal stricture and to provide the basis for clinical treatment. METHODS: We conducted a prospective, open-label, parallel-assignment randomized controlled trial with patients undergoing stent implantation for malignant esophageal stricture. The proximal segments of stents in the treatment group were fixed with 2 metal clips during the perioperative period of esophageal stent implantation, while no treatment was used in the control group. All patients underwent radiography at 3 and 7 days and 1 and 3 months after placement to assess the stent migration. RESULTS: There were 83 patients in our study. Demographic characteristics were similar between the groups. There was no stent migration observed in the treatment group within 2 weeks of the operation, while stent migration was observed in 6 of 41 (14.6%) cases in the control group, occurring at 3 and 7 days after placement. There were no perioperative complications. CONCLUSION: Perioperative fixation of the proximal segments of stents with metal clips is effective in preventing early stent migration.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".