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Record W2809872660 · doi:10.1177/1078155218784716

Real world evidence: Abiraterone use post-docetaxel in metastatic castrate-resistant prostate cancer

2018· article· en· W2809872660 on OpenAlexaboutno aff
Alisha Shivji, Raafi Ali, Scott North, Michael Sawyer, Sunita Ghosh, Carole Chambers

Bibliographic record

VenueJournal of Oncology Pharmacy Practice · 2018
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsnot available
Fundersnot available
KeywordsDocetaxelMedicineAbirateroneProstate cancerContext (archaeology)OncologyPharmacyAbiraterone acetateInternal medicineClinical trialChemotherapyCancerFamily medicineAndrogen deprivation therapy

Abstract

fetched live from OpenAlex

OBJECTIVES: The COU-AA-301 trial demonstrated that in men with metastatic castrate-resistant prostate cancer using abiraterone post-docetaxel increased overall survival. This study aims to assess this conclusion in a real world context. DESIGN: Retrospective chart review of a provincial Pharmacy BDM Database (a pharmacy dispensing software) and a provincial Electronic Chart (ARIA). Dispensing data, information on the state of the disease before and after abiraterone use, and information regarding effects of abiraterone were gathered. SETTING: Cancer centers in Alberta, Canada. PATIENTS: Metastatic castrate-resistant prostate cancer (CRPC) patients on abiraterone outside of a clinical trial who have previously had docetaxel chemotherapy for CRPC between February 2012 and May 2014. PRIMARY OUTCOME: Overall survival from the time of abiraterone initiation. RESULTS: Overall survival increase of 17 months was consistent with the survival increase of 14.8 months observed in the pivotal trial. CONCLUSION: Abiraterone is a valuable therapy post-docetaxel for metastatic CRPC, as in a real world context it demonstrated an increase in overall survival that was consistent with the findings of the clinical trial despite including a patient population of older age and lower performance status.

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)0.002
metaresearch head score (Gemma)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.619
Threshold uncertainty score0.811

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
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 head0.189
GPT teacher head0.520
Teacher spread0.331 · 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
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

Citations4
Published2018
Admission routes1
Has abstractyes

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