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Laparoscopic Nephrectomy for Infected, Obstructed and Non-functioning Kidneys

2004· article· en· W2072939288 on OpenAlexaff
Ran Katz, Dov Pode, Dragan Golijanin, Ofer N. Gofrit, Ofer Z. Shenfeld, Amos Shapiro, Petachia Reissman

Bibliographic record

VenueSurgical Laparoscopy Endoscopy & Percutaneous Techniques · 2004
Typearticle
Languageen
FieldMedicine
TopicPediatric Urology and Nephrology Studies
Canadian institutionsCegep de Sept Iles
Fundersnot available
KeywordsMedicineNephrectomySurgeryLaparoscopyPercutaneousLaparoscopic surgeryKidneyPyelogramInternal medicine

Abstract

fetched live from OpenAlex

Since laparoscopic nephrectomy was introduced by Clayman et al, it has been doubted whether it should be employed in patients with extensive perirenal fibrosis. In this series, 20 consecutive patients underwent laparoscopic nephrectomy for obstructed, infected, non-functioning kidneys. Preoperative assessment included urine cultures, abdominal sonography, intravenous pyelography, computerized tomography and a renal scan. Laparoscopic nephrectomies were performed using either the transperitoneal or the retroperitoneal approach.Patients' mean age was 52 years (range 20-77, SD = 15.2). Three patients underwent previous open surgery on the same kidney and 15 had percutaneous nephrostomies. The etiology of obstruction was stone disease in 15 cases, uretero-pelvic junction obstruction (3), iatrogenic ureteral injury (1), and infected multicystic kidney (1). Mean operative time was 224 minutes (range 140-325, SD = 57). Conversion to open surgery was necessary in one patient due to splenic injury. Mean hospital stay was 3 days (range 2-6, SD = 1). Laparoscopic nephrectomy was feasible in cases of severe perirenal fibrosis, with an acceptable rate of complications, and may be considered in patients with obstructed, infected, and non-functioning kidneys.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
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
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.009
GPT teacher head0.276
Teacher spread0.267 · 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 designBench or experimental
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

Citations10
Published2004
Admission routes1
Has abstractyes

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