Applications used to minimize forgotten stents: a systematic review of the literature. Is it the dawn of a new era?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Ureteral stents are an integral part of daily urological clinical practice, but in case of ureteral stents inadvertently left in place or forgotten, they can lead to a range of complications. Managing retained DJ stents presents a complex challenge for urologists, involving various aspects such as surgery, legal implications, and financial factors both for the patient as for the health system. Ensuring proper follow-up of patients poses a significant challenge even in nowadays in everyday clinical urological practice as it involves efficient urologist-patient communication. In modern society, smartphones have become an essential part of daily lives, providing a convenient and reliable way to store and access information through certain applications. Is it the best way to go for stent tracking? RECENT FINDINGS: We performed a systematic review of PubMed/Medline, EMBASE, Cochrane Library and Scopus and reference lists according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement. The tracking mechanisms include electronic medical records algorithms, mobile or chat applications, and computer-based applications. Prior to the implementation of a tracking system, hospitals experienced varying rates of stent loss or delayed removal, ranging from 0% to 13%. Through the implementation of a tracking mechanism, the occurrence of lost or delayed removal has been significantly reduced to 1%. SUMMARY: Stent tracking systems have proven to be highly effective in reducing the incidence of delayed removal of ureteral stents. Nevertheless, the widespread applicability of these systems is limited due to their primarily tailored design for institutions, while before implementing their use as a standard of care, more solid data through randomized trials is needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".