Bibliographic record
Abstract
Introduction:To compare outcomes of laparoscopic pyeloplasty in a cohort of children with 3 dimensional (3D) vision laparoscopy and articulating shears to a cohort with standard 2 dimensional (2D) laparoscopy.Methods: 33 patients with ureteropelvic junction obstruction who underwent laparoscopic pyeloplasty by a single surgoen from 2006 to 2013 were included.The current 3D cohort was compared to the previous 2D cohort, excluding cases from 2001-2005 to account for the learning curve.Excluded from the study were 3 cases of prior pyeloplasty, 2 because of ureteroscopy for fibroepithelial polyps and 1 open conversion in a duplex kidney with intrarenal pelvis.Patient age, weight, gender, side, operative time, dimension, presence of a crossing vessel, length of hospital stay and complication rate were compared between the 2 groups.Articulating shears were used for pelvotomy and spatulation of the ureter.Statistical tests included linear regression models and chi square tests using STATA.Results: The median age and weight of the population was 7.5 yrs and 28.5 kg, and 19 patients had a crossing vessel.Mean operative time for 2D (n=19) and 3D (n=8) cases was 265 minutes and 216 minutes.Operative time was decreased by an average of 48 minutes in the 3D group compared to the 2D group (p=0.02),even after adjusting for the presence of a crossing vessel (p=0.03).There was no difference in median age, weight, or presence of crossing vessel between both groups.3D did not affect Complication rate and length of hospital stay.The majority of 3D cases where performed using the laparoscopic flexible scissors, which was significantly associated with operative time (p=0.02). Conclusions:The use of 3D vision and articulating shears for pyeloplasty in children appears to significantly reduce operative time compared to conventional 2D vision with rigid scissors.This approach provides an alternative to current robotic assisted technology, warranting attention in view of significant cost saving.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.624 | 0.252 |
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".