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Record W2995069737 · doi:10.1159/000504787

Transverse Lumbotomy for Open Partial/Radical Nephrectomy: How I Do It

2019· article· en· W2995069737 on OpenAlexaff
Asmaa Ismail, Fabiola Oquendo, Erika Allard-Ihala, Hazem Elmansy, Walid Shahrour, Owen Prowse, Ahmed Kotb

Bibliographic record

VenueUrologia Internationalis · 2019
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsNOSM University
Fundersnot available
KeywordsMedicineNephrectomySurgeryKidneyOpen surgeryUrologyInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Conventional open surgical techniques allow proper surgical management for renal malignancies but have their intrinsic drawbacks. The aim of this paper is to present our technique in minimal renal exposure while avoiding the intrinsic complications of conventional techniques. METHODS: We described our technique, which can be easily understood and replicated by urologists performing open kidney surgery. RESULTS: Ninety-five patients had this technique done safely over the last 4 years, and 3 patients had this exposure changed into intraperitoneal extended wound for very large upper pole tumours. The median operating time was 70 min. No single patient required intraoperative blood transfusion. Median warm ischemic time was 9 min. CONCLUSION: Transverse lumbotomy is a safe reproducible technique that allows proper kidney exposure through a relatively smaller wound and avoiding unnecessary auxiliary techniques as rib resection, pleural tear management, and intraperitoneal exposure.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.044
GPT teacher head0.311
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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
Published2019
Admission routes1
Has abstractyes

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