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Record W1887176009 · doi:10.5489/cuaj.865

Hand-assisted laparoscopic ureteroureterostomy with renal mobilization for delayed recognition of a proximal ureteral injury after lumbar disk surgery

2013· article· en· W1887176009 on OpenAlexvenueno aff
Samuel D. Kaffenberger, Jeffrey Tomaszewski, Alice K. Tsao, Stephen V. Jackman

Bibliographic record

VenueCanadian Urological Association Journal · 2013
Typearticle
Languageen
FieldMedicine
TopicUreteral procedures and complications
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLumbarSurgeryMobilization

Abstract

fetched live from OpenAlex

We present hand-assisted laparoscopic ureteroureterostomy (HALUU) with renal mobilization as a novel approach to the management of proximal ureteral injury after lumbar disk surgery. A 63-year-old female underwent L4-L5 diskectomy and facetectomy with cage placement for back and leg pain. Postoperatively, she developed fever, nausea, abdominal pain, ileus and leukocytosis. A computed tomography scan of the abdomen and pelvis with intravenous contrast and delayed imaging demonstrated a left proximal ureteral injury with contrast extravasation. Retrograde and antegrade ureteral stent placement was unsuccessful; a nephrostomy tube was placed. Antegrade and retrograde ureterograms revealed a 3-cm proximal ureteral defect. All treatment options were discussed, and the patient chose to undergo hand-assisted laparoscopic renal mobilization with ureteroureterostomy, which was completed successfully without complications. Operative time was 381 minutes; estimated blood loss was 50 mL. The patient was discharged after 2 days, her ureteral stent was removed in 8 weeks, and follow-up with furosemide-mercaptoacetyltriglycine (MAG-3) renal scan demonstrated 30% function without evidence of obstruction. Hand-assisted laparoscopic ureteroureterostomy with renal mobilization can be performed as definitive management of a medium-length proximal ureteral injury. This is the first case describing this management technique after lumbar disk surgery.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.551

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.016
GPT teacher head0.227
Teacher spread0.211 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2013
Admission routes1
Has abstractyes

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