Robotic surgery for paediatric neurogenic lower urinary tract dysfunction: a systematic review
Bibliographic record
Abstract
OBJECTIVE: To evaluate in a systematic review the outcomes, benefits, and limitations of robot-assisted surgeries for paediatric neurogenic lower urinary tract dysfunction (LUTD), as robot-assisted techniques have emerged as a potential alternative, offering enhanced precision, dexterity, and visualisation. METHODS: This review was registered in the International Prospective Register of Systematic Reviews (PROSPERO identifier CRD42023464849) and adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We included studies of paediatric patients (aged <18 years) with neurogenic LUTD undergoing robot-assisted continence surgery, assessing safety and efficacy. Literature searches in the Medical Literature Analysis and Retrieval System Online (MEDLINE), Excerpta Medica dataBASE (EMBASE), and Scopus were conducted until 12 July 2024. Data extraction included surgical procedures, complications, operative times, lengths of stay, and bladder function outcomes. RESULTS: A total of 42 studies (20 case reports, 10 case series, six cohort studies, six comparative cohort studies) were included. Robotic procedures for continent catherisable channel construction, augmentation cystoplasty, and bladder neck reconstruction showed comparable peri- and postoperative outcomes. Meta-analysis of five studies comparing robotic vs open appendicovesicostomy indicated a significant reduction in length of stay for robotic groups, while operative time, complications, and re-intervention rates were not significantly different. Conversions to open surgery were rare, indicated by adhesions or small appendices during channel constructions. CONCLUSIONS: Robot-assisted surgeries for paediatric neurogenic LUTD demonstrate potential benefits, including reduced hospital stays and comparable complication rates to open surgery in certain contexts. However, the available evidence is limited by heterogeneity in study designs, small sample sizes, and single-centre experiences, which constrain generalisability. Standardised reporting of complications and outcomes, alongside multicentre studies, is essential to clarify the long-term efficacy and broader applicability of these techniques.
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 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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".