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Impact of early identification of brain metastases in metastatic renal cell carcinoma (mRCC).

2020· article· en· W3007455364 on OpenAlexaffabout
Ambika Parmar, Sunita Ghosh, Aly‐Khan A. Lalani, Aaron R. Hansen, M. Neil Reaume, Lori Wood, Naveen S. Basappa, Daniel Yick Chin Heng, Jeffrey Graham, Christian Kollmannsberger, Denis Soulières, Rodney H. Breau, Simon Tanguay, Anil Kapoor, Frédéric Pouliot, Georg A. Bjarnason

Bibliographic record

VenueJournal of Clinical Oncology · 2020
Typearticle
Languageen
FieldMedicine
TopicBrain Metastases and Treatment
Canadian institutionsSunnybrook HospitalMcMaster UniversityMcGill UniversityUniversity of OttawaUniversité LavalCentre Hospitalier de l’Université de MontréalPrincess Margaret Cancer CentreUniversity of CalgaryJuravinski Cancer CentreUniversity of ManitobaHealth Sciences CentreUniversity Health NetworkOttawa HospitalUniversity of AlbertaDalhousie UniversitySunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineAsymptomaticInterquartile rangeRenal cell carcinomaHazard ratioInternal medicineCohortProportional hazards modelPathologicalDiseaseSurgeryConfidence interval

Abstract

fetched live from OpenAlex

620 Background: Up to one-third of patients with mRCC can present with asymptomatic brain metastases (BM). Timely identification of BM allows for the delivery of early local interventions, which may lead to improved patient outcomes. To investigate the potential utility of routine intra-cranial imaging, we compared the outcomes of mRCC patients with asymptomatic versus symptomatic BM. Methods: Using the Canadian Kidney Cancer information system (CKCis) database, we identified mRCC patients diagnosed with BM between 2011 and 2018. This cohort was divided into two groups dependent on the presence or absence of neurological symptoms. Baseline patient demographics, clinico-pathological disease characteristics and survival data were extracted. Statistical analysis was through chi-square tests, analysis of variance and Kaplan-Meier method to characterize survival outcomes. Results: 269 mRCC patients with BM were identified with the majority presenting with symptomatic disease (n=163; 61%). No significant differences in clinico-pathological disease characteristics were identified. Median overall survival (OS) from mRCC diagnosis for asymptomatic patients was 33.4 months (interquartile range, IQR 27.8-64.4) versus 34.5 months (20.4-43.4) for symptomatic patients (p=0.35). Median OS from time of BM diagnosis revealed a trend favoring asymptomatic, as compared to symptomatic, patients [24.5 (17.6-24.9) vs. 13.1 months (9.0-20.6), p=0.06]. Factors associated with worse OS from time of BM diagnosis included presentation with symptomatic BM [hazard ratio, HR (95% CI): 1.40 (1.03-1.90), p=0.034] and International Metastatic Renal Cell Carcinoma Consortium Database (IMDC)-characterized intermediate/poor risk disease [HR (95% CI): 1.47 (1.01-2.13), p=0.045]. Conclusions: Routine intra-cranial imaging may lead to earlier identification of BM in mRCC. However, further investigation as to whether this practice improves survival is warranted.

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)0.001
metaresearch head score (Gemma)0.006
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 score0.025
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.166
GPT teacher head0.483
Teacher spread0.317 · 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
Published2020
Admission routes2
Has abstractyes

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