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Record W1964762710 · doi:10.1097/spc.0b013e32832e9c6d

The current management of small renal masses

2009· review· en· W1964762710 on OpenAlexaff
Quoc‐Dien Trinh, Fred Saad, Jean‐Baptiste Lattouf

Bibliographic record

VenueCurrent Opinion in Supportive and Palliative Care · 2009
Typereview
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsHôpital Saint-LucUniversité de Montréal
Fundersnot available
KeywordsMedicineCryoablationNatural historyRadiologyMicrowave ablationAblative caseAblationKidney cancerBiopsyCryosurgeryIntensive care medicineNephrectomyGeneral surgeryKidney diseaseKidneySurgeryInternal medicineRadiation therapy

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Small renal masses are increasingly being discovered incidentally on routine abdominal imaging performed for other purposes. The natural history of small renal masses as well as the therapeutic approach for such lesions is an evolving paradigm. In this review, we assess the current literature regarding this controversial topic. RECENT FINDINGS: Nephron-sparing surgery can be performed with minimal morbidity and mortality. Ablative techniques, such as cryoablation and radio frequency ablation, have shown both safety and reliability in series reporting short-term oncologic follow-up. Preliminary studies seem to favor cryoablation when compared with radio frequency ablation. Observational series have shown that very few of these selected patients present disease progression while on active surveillance. Finally, recent studies have shown that renal biopsy is both well tolerated and accurate. SUMMARY: The management of small renal masses remains a challenging issue. Although nephron-sparing surgery is the standard of care for these masses, ablative techniques and observation represent adequate alternatives, especially for poor surgical candidates. Renal biopsy is increasingly proving to be of value in the management of small renal masses.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.939
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.201
GPT teacher head0.435
Teacher spread0.233 · 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 designOther design
Domainnot available
GenreReview

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
Published2009
Admission routes1
Has abstractyes

Explore more

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