OPTIMAL PREVENTION AND MANAGEMENT OF PROXIMAL URETERAL STENT MIGRATION AND REMIGRATION
Bibliographic record
Abstract
PURPOSE: We evaluated issues associated with proximal ureteral stent migration and remigration, including causes and management, and the predictability of ureteral length. MATERIALS AND METHODS: All proximal ureteral stent migrations that occurred from January 1997 to March 2000 were reviewed. Characteristics and treatment of the 33 patients with proximal ureteral stent migration were compared with those of 66 randomly selected controls who did not have stent migration. We also analyzed a subgroup of 6 cases of remigration. RESULTS: Of the ureteral stents 2% migrated proximally. Mean height was greater in patients with versus without a migrated stent (p = 0.028). The stent-to-ureter length ratio was lower in the migrated than in the nonmigrated group (p <0.0001). Patient height and side of migration were significant predictors of ureteral length (R2 = 0.3511, p <0.0001 and 0.0007, respectively). Of the patients who required continued ureteral stenting migrated stent management included placement of a longer stent in 9 (group 1) and a stent of equal length in 4 (group 2), and repositioning of the original stent in 4 (group 3). There was no remigration in group 1. However, migration recurred in 2 patients in group 2 (50%) and in all 4 in group 3 (100%). CONCLUSIONS: Proximal migration occurs when a stent is too short for the ureter. We recommend that ureteral length should be measured directly from an x-ray to select the optimal stent length. If it is necessary to continue stenting a ureter after migration has been detected, a longer stent should be placed.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".