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Nephron-Sparing Surgery in Renal Cell Carcinoma: Morbidity and Outcomes

2013· article· en· W3140563178 on OpenAlexvenueno aff
Marcos F. Dall’Oglio, Alexandre Crippa, José R. Colombo, Rafael F. Coelho, Eder Nisi Ilário, Miguel Srougi

Bibliographic record

VenueJournal of cancer research updates · 2013
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineNephrectomyRenal cell carcinomaPerioperativeAdipose capsule of kidneyPathologicalSurgeryCancerKidney cancerKidneyUrologyInternal medicine

Abstract

fetched live from OpenAlex

Objective: To present the partial nephrectomy series performed at our institution. Patients and Methods: 147 patients underwent nephron-sparing surgery between Jan/2000 and Feb/2011. The mean patient age was 60.3 yrs (33.2-82.7), and 90 (61.2%) were men. The clinical presentation, pathological tumor features, perioperative complications, functional and oncological outcomes were analyzed. Results: 84.4% of the renal masses were incidental, and the mean tumor size was 3,63 cm. Median warm ischemia time and estimated blood loss was 18 min (11-27) and 220 ml (50-480), respectively. Overall complication rate was 5%. 87.0% of the tumors were pT1, 5.7% were pT2, and 7.3% was pT3. 45 tumors were high-grade (30.6%), microvascular invasion was observed in eleven tumors (7.5%), presence of necrosis occurred in twenty-seven tumors (18.4%), and invasion of perirenal fat was identified in ten cases (6.8%). At a mean follow-up of 60 months, local recurrence was observed in only six cases (4.1%) and the cancer-specific survival in this series was 95.2%. Conclusion: Open partial nephrectomy is safe and presented optimal oncological results. It should be used for treating small renal tumors whenever is technically feasible.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.081
GPT teacher head0.369
Teacher spread0.288 · 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 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".

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Citations1
Published2013
Admission routes1
Has abstractyes

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