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Record W2071155427 · doi:10.1155/2014/354104

Laparoscopic Nephrectomy,<i>Ex Vivo</i>Partial Nephrectomy, and Autotransplantation for the Treatment of Complex Renal Masses

2014· article· en· W2071155427 on OpenAlexafffund
Jasmir G. Nayak, Joshua Koulack, Thomas McGregor

Bibliographic record

VenueCase Reports in Urology · 2014
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsUniversity of Manitoba
FundersCanadian Urological Association
KeywordsMedicineNephrectomyAutotransplantationRenal cell carcinomaSurgeryRenal functionKidneyEx vivoUrologyTransplantationIn vivoInternal medicine

Abstract

fetched live from OpenAlex

In the contemporary era of minimally invasive surgery, very few T1/T2 renal lesions are not amenable to nephron-sparing surgery. However, centrally located lesions continue to pose a clinical dilemma. We sought to describe our local experience with three cases of laparoscopic nephrectomy, ex vivo partial nephrectomy, and autotransplantation. Laparoscopic donor nephrectomy was performed followed by immediate renal cooling and perfusion with isotonic solution. Back-table partial nephrectomy, renorrhaphy, and autotransplantation were then performed. Mean warm ischemia (WIT) and cold ischemic times (CIT) were 2 and 39 minutes, respectively. Average blood loss was 267 mL. All patients preserved their renal function postoperatively. Final pathology confirmed pT1, clear cell renal cell carcinoma with negative margins in all. All are disease free at up to 39 months follow-up with stable renal function. In conclusion, the described approach remains a viable option for the treatment of complex renal masses preserving oncological control and renal function.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.556
Threshold uncertainty score0.509

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 head0.034
GPT teacher head0.297
Teacher spread0.264 · 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

Citations6
Published2014
Admission routes2
Has abstractyes

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