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Record W2324806156 · doi:10.1097/sle.0b013e31828e3f18

Pediatric Robotic Pyeloplasty in Patients Weighing Less Than 10kg Initial Experience

2014· article· en· W2324806156 on OpenAlexaff
Glória Pelizzo, Ghassan Nakib, Ilaria Goruppi, Luigi Avolio, Piero Romano, Alessandro Raffaele, Federico Scorletti, Simonetta Mencherini, Valeria Calcaterra

Bibliographic record

VenueSurgical Laparoscopy Endoscopy & Percutaneous Techniques · 2014
Typearticle
Languageen
FieldMedicine
TopicPediatric Urology and Nephrology Studies
Canadian institutionsPediatric Oncology Group
Fundersnot available
KeywordsMedicinePyeloplastyUreteropelvic junctionSurgeryFibrous jointStentGeneral surgeryUrinary systemHydronephrosisInternal medicine

Abstract

fetched live from OpenAlex

AIM: To report the feasibility and safety of a robotic-assisted laparoscopic pyeloplasty (RALP) in patients weighing <10 kg. MATERIALS AND METHODS: Three patients weighing between 5 and 8 kg who were affected by severe congenital ureteropelvic junction obstruction, including a child with solitary kidney, were subjected to RALP. Three trocars were placed; sutures and pyeloplasty remodeling were performed with interrupted stitches. A double J stent was inserted through a 2-mm transparietal angiocatheter to protect the pyelic suture. RESULTS: The procedures were all completed within 90 minutes, the "docking" time requiring 20 minutes. The patients were discharged on postoperative day 2, without any complications. CONCLUSIONS: Comprehensive assessment of pyelic suture in a very narrow field with 2 operative instruments is feasible and safe. Robotic pyeloplasty provides all the advantages of mini-invasive surgery with the added advantage of higher magnification and excellent surgical navigation in very small spaces and on fragile infant tissues.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.730
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.287
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2014
Admission routes1
Has abstractyes

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