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Record W4241454646 · doi:10.3747/co.v18is2.958

Progression-Free Survival as a Clinical Trial Endpoint in Advanced Renal Cell Carcinoma

2011· article· en· W4241454646 on OpenAlexaffvenue
Sebastién J. Hotte, G.A. Bjarnason, D.Y.C. Heng, M.A.S. Jewett, A. Kapoor, Christian Kollmannsberger, J. Maroun, L.A. Mayhew, S. North, M.N. Reaume, J.D. Ruether, D. Soulieres, P.M. Venner, E.W. Winquist, L. Wood, J.H.E. Yong, F. Saad

Bibliographic record

VenueCurrent Oncology · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsCanadian Centre for Applied Research in Cancer ControlUniversity of TorontoWestern UniversityCentre Hospitalier de l’Université de MontréalUniversity Health NetworkOttawa HospitalOttawa Regional Cancer FoundationUniversity of OttawaBC Cancer AgencyPrincess Margaret Cancer CentreSt. Michael's HospitalSunnybrook Health Science CentreJuravinski Cancer Centre
Fundersnot available
KeywordsMedicineClinical endpointClinical trialRenal cell carcinomaIntensive care medicineProgression-free survivalOncologyEndpoint DeterminationOverall survivalDiseaseSurrogate endpointInternal medicine

Abstract

fetched live from OpenAlex

Traditionally, overall survival (os) has been considered the “gold standard” for evaluating new systemic oncologic therapies, because death is easy to define, is easily compared across disease sites, and is not subject to investigator bias. However, as the available options for continuing therapy increase, the use of os as a clinical trial endpoint has become problematic because of the increasing crossover and contamination of trials. As a result, the approval of promising new therapies may be delayed. Many clinicians believe that progression-free survival (pfs) is a more viable option for evaluating new therapies in metastatic and advanced renal cell carcinoma. As with all endpoints, pfs has inherent biases, and those biases must be addressed to ensure that trial results are not compromised and that they will be accepted by regulatory authorities. In this paper, we examine the issues surrounding the use of pfs as a clinical trial endpoint, and we suggest solutions to ensure that data integrity is maintained.

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)0.286
metaresearch head score (Gemma)0.353
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.286
Threshold uncertainty score0.881

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2860.353
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.003
Science and technology studies0.0010.003
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0020.003
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 head0.761
GPT teacher head0.580
Teacher spread0.181 · 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.

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

Citations37
Published2011
Admission routes2
Has abstractyes

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