MétaCan
Menu
Back to cohort
Record W4386591643 · doi:10.2217/imt-2023-0039

Association Between Age and Efficacy of First-Line Immunotherapy-Based Combination Therapies for Mrcc: A Meta-Analysis

2023· review· en· W4386591643 on OpenAlexaff
Takafumi Yanagisawa, Fahad Quhal, Tatsushi Kawada, Kensuke Bekku, Ekaterina Laukhtina, Paweł Rajwa, Markus von Deimling, Marcin Chłosta, Benjamin Pradère, Pierre I. Karakiewicz, Keiichiro Mori, Takahiro Kimura, Manuela Schmidinger, Shahrokh F. Shariat

Bibliographic record

VenueImmunotherapy · 2023
Typereview
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicineMeta-analysisRenal cell carcinomaSunitinibHazard ratioInternal medicineKidney cancerImmunotherapyOncologyCancerConfidence interval

Abstract

fetched live from OpenAlex

Aim: To compare the efficacy of first-line immune checkpoint inhibitor (ICI)-based combinations in metastatic renal cell carcinoma (mRCC) patients stratified by chronological age. Methods: According to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, hazard ratios for overall survival (OS) from randomized controlled trials were synthesized. Results: Five RCTs were eligible for meta-analyses. ICI-based combinations significantly improved OS compared with sunitinib alone, both in younger (<65 years) and older (≥65 years) patients, whereas the OS benefit was significantly better in younger patients (p = 0.007). ICI-based combinations did not improve OS in patients aged ≥75 years. Treatment rankings showed age-related differential recommendations regarding improved OS. Conclusion: OS benefit from first-line ICI-based combinations was significantly greater in younger patients. Age-related differences could help enrich shared decision-making.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.438
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.006
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.157
GPT teacher head0.382
Teacher spread0.225 · 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 designMeta-analysis
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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueImmunotherapySame topicRenal cell carcinoma treatmentFrench-language works237,207