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The potential role of frailty in modifying quality-of-life related outcomes in metastatic castration-resistant prostate cancer (mCRPC) patients receiving docetaxel, abiraterone, enzalutamine, and radium 223.

2021· article· en· W3134108600 on OpenAlexafffund
Seungyeon Kim, Henriette Breunis, Narhari Timilshina, Helen Yang, Shabbir M.H. Alibhai

Bibliographic record

VenueJournal of Clinical Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsUniversity Health NetworkUniversity of Toronto
FundersProstate Cancer Canada
KeywordsMedicineProstate cancerQuality of life (healthcare)DocetaxelMoodEnzalutamideCohortInternal medicinePhysical therapyOncologyCancerPsychiatry

Abstract

fetched live from OpenAlex

63 Background: As the arsenal of therapeutic agents for mCRPC expands, frailty-informed care is emerging as a strategy for tailoring management decisions. Yet, data to guide this approach and to ascertain its impact on the mood, fatigue, pain, and quality of life (QoL) experienced by older men with mCRPC remain lacking. We examined patient-reported outcomes, stratified by a validated frailty index (FI), for mCRPC treatment with docetaxel chemotherapy (CHEMO), abiraterone (ABI), enzalutamide (ENZA), or radium 223 (RAD). Methods: Older (aged 65+) men starting one of four approved therapies for mCRPC were enrolled in a multicenter prospective cohort study. Assessing their mood, fatigue, pain, and QoL with PHQ-9, ESAS tiredness, ESAS pain, and FACT-G total as well as subscale scores, we used linear mixed effect models to determine change in each outcome over time (0, 3, 6 months). At end of treatment, we administered the Decisional Regret Scale. We then constructed a FI from 34 variables that span laboratory abnormalities, geriatric syndromes, instrumental activities of daily living, social support, as well as emotional, cognitive, and physical deficits. Following established cut-offs, we categorized patients as non-, pre-, and frail, then performed stratified linear mixed effects regression analyses to identify differences in outcomes by frailty status. Results: A total of 198 men (mean age 75.1) starting CHEMO (n = 70), ABI (n = 38), ENZA (n = 67), and RAD (n = 29) were included, of which 9.6%, 1.5%, 5.6%, and 2.5%, respectively, were determined frail. Frailty correlated only modestly with age (Pearson r = 0.27). Independent of frailty status, patients across treatment cohorts were similar in terms of baseline QoL-related measures, with the exceptions of mood (p = 0.033) and pain (p = 0.034). Over time, no significant change in QoL was reported, although all four therapies resulted in generally low levels of decisional regret and similar trends of improved pain but worsened mood (p = 0.006 and 0.02, respectively). At baseline, frailty status correlated with worse FACT-G total (p < 0.001) and functional well-being (p < 0.001), as well as worse depression scores (p < 0.001). According to FI-stratified analysis, frail patients experienced similar QoL-related outcomes to fit patients for all measures aside from mood (p < 0.001). Contrary to our hypotheses, frailty was not associated with significant worsening in emotional well-being or functional well-being in response to mCRPC treatment. Conclusions: Of the older men receiving care for mCRPC, frail patients may experience generally similar trends in QoL as fit patients. Interestingly, frailty status, rather than treatment modality, may play more of a contributory role to changes in QoL-related outcomes 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

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)0.001
metaresearch head score (Gemma)0.003
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.104
GPT teacher head0.449
Teacher spread0.345 · 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".

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Citations0
Published2021
Admission routes2
Has abstractyes

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