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Record W4387320129 · doi:10.48083/ueml5802

Ablative Therapies for Localized Primary Renal Cell Carcinoma

2022· article· en· W4387320129 on OpenAlexaffvenue
Muhammad Ali, Vanessa Acosta Ruiz, Sarah P. Psutka, David Liu, Shankar Siva

Bibliographic record

VenueSociété Internationale d’Urologie Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsUniversity of British Columbia
FundersCancer Council VictoriaAustralian Government
KeywordsCryoablationSABR volatility modelMedicineMicrowave ablationRenal cell carcinomaAblative caseRadiofrequency ablationNephrectomyAblationSurgeryRadiation therapyRadiosurgeryRadiologyKidneyUrologyInternal medicine

Abstract

fetched live from OpenAlex

Surgery with either partial or radical nephrectomy remains the standard of care for localized primary renal cell carcinoma (RCC). However, most RCCs are detected in an older age group, and some may have multiple comorbidities that preclude surgery. Thermal ablation (TA) with radiofrequency ablation (RFA), cryoablation (CA), or microwave ablation (MWA) is considered an alternative to extirpative surgical procedures for select patients with small renal tumors. There is more than 90% post-ablation local control in carefully selected patients with reported complication rates of less than 10%. Most thermal ablation require only a single procedure. More recently, stereotactic ablative body radiotherapy (SABR) has emerged as an attractive noninvasive treatment modality for elderly patients with comorbidities and localized RCC. It has shown more than 90% local control rates for both small and relatively larger tumors (> 4 cm). Modest post-SABR renal function decline has been observed. Despite most patients presenting with mild or moderate chronic kidney disease there is less than a 5% chance of progression to end-stage renal disease. This article aims to summarize the key evidence and ablative treatment’s optimal patient selection, efficacy, and toxicity.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.192
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.045
GPT teacher head0.303
Teacher spread0.258 · 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 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

Citations13
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueSociété Internationale d’Urologie JournalSame topicRenal cell carcinoma treatmentFrench-language works237,207