Predictive factors for early discharge (≤24 hours) and re-admission following robotic-assisted laparoscopic pyeloplasty in children
Bibliographic record
Abstract
INTRODUCTION: Minimally invasive pyeloplasty (MIP) for correction of ureteropelvic junction obstruction in children has significantly improved the postoperative management of these patients. In this study, we sought to examine the factors associated with early discharge (≤24 hours) in children who underwent robotic-assisted laparoscopic pyeloplasty (RALP). METHODS: We performed a retrospective chart review of all children who underwent RALP from 2012-2018 in our center. Descriptive statistics and a non-adjusted risk analysis were performed to evaluate the factors associated with early discharge (≤24h), re-admission, and complications within the first 30 days after the procedure. RESULTS: Eighty-nine patients out of 124 total pyeloplasties (72%) stayed ≤24 hours post-surgery. Of the variables analyzed, later cases were statistically associated with length of stay (LOS); the first 55 patients had a lower probability of being hospitalized for ≤24 hours (odds ratio [OR] 0.24, 95% confidence interval [CI] 0.09-0.64, p=0.004). CONCLUSIONS: RALP for children is associated with a high rate of early recovery, short hospital stay, and low re-admission and complication rates. Although not statistically significant, patients with shorter operative room time also had a shorter LOS. An increased LOS was observed in the initial patients of our series, and this is most likely explained by the initial learning curve of the team for the procedure itself and the more conservative postoperative management.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".