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Record W2888229344 · doi:10.1177/1756287218794094

The contemporary role of lymph node dissection in the management of renal cell carcinoma

2018· review· en· W2888229344 on OpenAlexaff
Piotr Zareba, Jehonathan H. Pinthus, Paul Russo

Bibliographic record

VenueTherapeutic Advances in Urology · 2018
Typereview
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsMcMaster UniversityJuravinski Hospital
FundersNational Cancer Institute
KeywordsMedicineRenal cell carcinomaLymph nodeNephrectomyDissection (medical)DiseaseSystemic therapyOncologyKidney cancerInternal medicineAdjuvantRandomized controlled trialClinical trialCancerSurgeryKidney

Abstract

fetched live from OpenAlex

The appropriate role of lymph node dissection (LND) in the management of patients with renal cell carcinoma (RCC) is still a matter of debate. There is ample evidence that LND is the most accurate modality for staging the regional lymph nodes (LNs), which may harbor metastatic disease in greater than one-third of patients with high-risk RCC. The presence of LN metastases is an independent negative prognostic factor in this disease and accurate determination of LN status not only helps with patient counselling regarding prognosis and tailoring of postoperative surveillance schedules, but it also identifies patients at high risk of systemic disease recurrence who may qualify for clinical trials of adjuvant systemic therapies. Meanwhile, the therapeutic value of LND has been brought into question by a randomized trial (European Organisation for Research and Treatment of Cancer; EORTC 30881) that showed no difference in progression-free or overall survival between patients who were treated with radical nephrectomy (RN) and LND and those treated with RN alone. Given that most patients enrolled in this trial had small renal masses and therefore were at low risk for LN metastases, the question of whether patients with high-risk tumors derive a therapeutic benefit from a standardized, extended LND remains unanswered.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score0.693

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.039
GPT teacher head0.328
Teacher spread0.289 · 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 designNot applicable
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

Citations9
Published2018
Admission routes1
Has abstractyes

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