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Effect of abiraterone acetate (AA) and enzalutamide (EZ) administration on chemotherapy-naïve metastatic castration-resistant prostate cancer (CN-mCRPC) patient’s symptom burden.

2016· article· en· W2892272740 on OpenAlexaffabout
Sepehr Salem, Maria Komisarenko, Narhari Timilshina, Frank Jiao, Ruby Grewal, Shabbir M.H. Alibhai, Antonio Finelli

Bibliographic record

VenueJournal of Clinical Oncology · 2016
Typearticle
Languageen
FieldMedicine
TopicCancer Treatment and Pharmacology
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineEnzalutamideProstate cancerInternal medicineMultivariate analysisAbiraterone acetateUnivariate analysisCancerChemotherapyOncologyAndrogen deprivation therapy

Abstract

fetched live from OpenAlex

e16540 Background: Reliable evaluation of the disease burden throughout advanced PC is crucial to the assessment of treatment effectiveness and patient improvement. This study sought to further explore and compare the effect of AA and EZ administration on CN-mCRPC patient’s symptom burden using the Edmonton Symptom Assessment System (ESAS). Methods: 245 CN-mCRPC patients who had received AA (93) or EZ (152) at our center were included and their associated ESAS scores, baseline demographic information, comorbidities, Eastern Cooperative Oncology Group performance status (PS) and laboratory data along with pharmacological interventions were recorded. Univariate and multivariate linear regression analysis were conducted to determine and compare the impact of drug therapy on ESAS items. The minimal clinically important difference was also assessed. Results: Mean (SD) treatment duration in AA and EZ was 9.6 (6.8) and 9.4 (6.8) months, respectively. There were no statistically significant differences in the proportion of patients with clinically meaningful symptom improvement/deterioration following AA or EZ administration in all ESAS-based physical and psychological symptoms. Fatigue was rated the most distressing before and after treatment in both groups. In multivariate analysis, drug initiation, drowsiness and poor well-being were the most significant predictors of fatigue in the AA group, while in the EZ group, the factors were PS ≥ 2, prior treatment procedure, drug initiation, pain, drowsiness, dyspnea and poor well-being. Comparing coefficients, drug initiation and PS created the greatest worsening impact on fatigue in the AA and EZ groups, respectively. Clinically meaningful improvement, deterioration and no change in fatigue were reported in 13%, 46% and 41% of AA, and 16%, 44% and 40% of EZ group, respectively (p > 0.05). Median time to worsening of fatigue was 183 and 132 days following AA and EZ, respectively (p > 0.05). Conclusions: In CN-mCRPC patients, effects of AA and EZ on physical and psychological symptoms assessed by ESAS were comparable. Furthermore, fatigue progressed similarly in both groups over time.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Other designlow
models splitAgreement compares identical category sets and study designs across arms.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.063
GPT teacher head0.471
Teacher spread0.409 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational · Other design
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
Published2016
Admission routes2
Has abstractyes

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