Predictive Factors of Successful Double J Stent Insertion Among Advanced Cervical Cancer Patients
Bibliographic record
Abstract
Background: Cervical cancer remains the most lethal and prevalent cancer among women. Obstructive uropathy is a common complication of advanced cervical cancer, caused by the expanding tumor. One of the recommended treatments for this condition is the implantation of a double J (DJ) stent. However, this procedure is challenging due to the unique characteristics of the patient. The objective of this study was to identify the variables that influence the successful insertion of a DJ stent in women with advanced cervical cancer. Methods: This retrospective study included women who attempted to have a DJ stent implanted at the General Hospital of Adam Malik in Medan, Indonesia, between January 2020 and December 2022, and were diagnosed with advanced cervical cancer. The inclusion criteria were limited to cervical cancer patients in stages III-IV, according to the International Federation of Gynecology and Obstetrics (FIGO) staging standard, who underwent an attempt at DJ stent insertion. Patients who underwent a nephrostomy and received a DJ stent were excluded from the study. The participants were divided into two groups based on the success of the DJ stent implantation. The analysis was conducted using the logistic regression test and the Chi-square test. Results: The study included 88 patients with advanced-stage cervical cancer, of whom 45 underwent nephrostomy and 43 received a DJ stent. The analysis revealed that lower levels of hydronephrosis (odds ratio (OR): 18.203, P = 0.001), urea (OR: 4.207, P = 0.037), and creatinine (OR: 6.923, P = 0.004), higher levels of urine output (OR: 8.26, P = 0.003), and lower cervical cancer stage (OR: 4.125, P = 0.022) were all predictors of successful DJ stent insertion. Conclusion: For women with advanced cervical cancer, lower degrees of hydronephrosis, urea, and creatinine levels, higher urine output, and lower cervical cancer stage were all predictive factors for successful DJ stent implantation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".