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Impact of timing of immunotherapy and cytoreductive nephrectomy on outcomes in metastatic renal cell carcinoma: Results from the CKCis database.

2024· article· en· W4391303450 on OpenAlexaffabout
Changsu Park, Sunita Ghosh, Feras A. Moria, Lori Wood, Georg A. Bjarnason, Bimal Bhindi, Daniel Yick Chin Heng, Vincent Castonguay, Frédéric Pouliot, Christian Kollmannsberger, Dominick Bossé, Naveen S. Basappa, Antonio Finelli, Nazanin Fallah‐Rad, Rodney H. Breau, Aly‐Khan A. Lalani, Simon Tanguay, Jeffrey Graham, Ramy Saleh

Bibliographic record

VenueJournal of Clinical Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsUniversity of ManitobaMcMaster UniversityOttawa HospitalMcGill University Health CentreMcGill UniversityUniversity of OttawaHôtel-Dieu de QuébecBC Cancer AgencySunnybrook Health Science CentreUniversité LavalUniversity of AlbertaQueen Elizabeth II Health Sciences CentreUniversity of TorontoDalhousie UniversityPrincess Margaret Cancer CentreUniversity of Calgary
Fundersnot available
KeywordsMedicineRenal cell carcinomaImmunotherapyNephrectomyInternal medicineUrologyOncologyDatabaseKidneyCancer researchCancer

Abstract

fetched live from OpenAlex

416 Background: Immunotherapy-based systemic treatment (ST) is the standard of care for most patients diagnosed with metastatic renal cell carcinoma (mRCC). Cytoreductive nephrectomy (CN) has historically shown benefit for select patients with mRCC but its role and timing are not well-understood in the era of immunotherapy. The primary objective of this study is to assess patient outcomes in patients who received ST only, CN followed by ST (CN-ST) and ST followed by CN (ST-CN). Methods: The Canadian Kidney Cancer information system (CKCis) database was queried to identify patients with de novo mRCC who received immunotherapy-based ST for mRCC between January 2014 to June 2023. These patients were classified into three categories as described above. Cox proportional hazards models were used to assess the impact of the timing of ST and CN on overall survival (OS) and progression free survival (PFS), after adjusting for IMDC risk group. Complications of ST and CN for these cohorts were collected. Results: A total of 588 patients were included in this study. 331 patients received ST only, 215 patients received CN-ST and 42 patients received at least one dose of ST prior to CN. Patient and disease characteristics including age, gender, performance status, IMDC risk category, comorbidity, histology, type of ST and metastatic sites are reported and globally well-balanced. OS analysis favoured patients who received ST-CN (hazard ratio [HR] 0.30, 95% confidence interval [CI] 0.13-0.68) and CN-ST (HR 0.68, CI 0.47-0.97) over patients who received ST only. PFS analysis showed a similar trend for ST-CN (HR 0.45, CI 0.26-0.77) and CN-ST (HR 0.9, CI 0.68-1.17). The most common cause of ST delay or cessation was treatment toxicity, followed by progression of disease. The most common perioperative complication was bleeding, followed by infection. Conclusions: This study examined baseline features and outcomes associated with the use and timing of CN and ST using real world data through the CKCis database. Patients selected to receive CN after ST seem to have improved outcomes. There were no appreciable differences in ST toxicity or perioperative complications across groups. Limitations include the small number of patients in the CN-ST group and residual confounding and selection bias that may influence the outcomes in patients undergoing CN.

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)0.002
metaresearch head score (Gemma)0.008
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 score0.503
Threshold uncertainty score1.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.162
GPT teacher head0.468
Teacher spread0.307 · 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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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