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Record W2036674153 · doi:10.5489/cuaj.1775

Ex-vivo nephron-sparing surgery and autotransplantation for renal tumours: Revisited

2014· article· en· W2036674153 on OpenAlexvenueno aff
George P. Abraham, Avinash T. Siddaiah, Krishnamohan Ramaswami, Datson P. George, Krishanu Das

Bibliographic record

VenueCanadian Urological Association Journal · 2014
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsnot available
Fundersnot available
KeywordsAutotransplantationMedicineNephrectomySurgeryLaparoscopyEx vivoUrologyRenal cell carcinomaKidneyNephronIn vivoTransplantationInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: We studied the feasibility of ex-vivo nephron-sparing surgery and autotransplantation for complex renal tumours. We also studied the role of laparoscopy in these situations. METHODS: All patients who underwent renal autotransplantation for renal tumour at our centre were included in this retrospective study. Patient profiles were recorded in detail. Operative and postoperative details were also recorded. RESULTS: Our series includes 3 patients. Two patients had complex renal cell carcinoma and 1 patient had bilateral large angiomyolipoma. In first 2 patients, laparoscopic approach was used for nephrectomy. Operative time for case 1, 2 and 3 was 5.5, 4.5, 8 (right side) and 6 (left side) hours, respectively. Cold ischemia time was 110, 90, 150 and 125 minutes, respectively. One patient required temporary postoperative hemodialysis. CONCLUSION: Ex-vivo nephron-sparing surgery and autotransplantation still remain a viable option for complex renal tumours. It offers satisfactory renal functional outcome with acceptable morbidity. The laparoscopic approach should be used whenever possible to reduce morbidity.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.018
Threshold uncertainty score0.450

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.019
GPT teacher head0.226
Teacher spread0.206 · 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

Citations19
Published2014
Admission routes1
Has abstractyes

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