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Understanding treatment toxicity patterns through remote symptom monitoring and examining the effect of frailty in older men with metastatic prostate cancer.

2023· article· en· W4379346764 on OpenAlexafffundabout
Milothy Parthipan, Gregory Feng, Henriette Breunis, Narhari Timilshina, Enrique Soto‐Pérez‐de‐Celis, Aaron R. Hansen, Urban Emmenegger, Antonio Finelli, Padraig Warde, George Tomlinson, Monika K. Krzyzanowska, Andrew Matthew, Hance Clarke, Daniel Santa Mina, Martine Puts, Shabbir M.H. Alibhai

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsUniversity of TorontoSunnybrook Health Science CentrePrincess Margaret Cancer CentreHealth Sciences CentreUniversity Health Network
FundersProstate Cancer Canada
KeywordsMedicineProstate cancerCohortProspective cohort studyInternal medicineCancerGeriatric oncologyPhysical therapy

Abstract

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12054 Background: Advancements in metastatic prostate cancer (mPC) treatments have increased survival, but using multiple lines of therapy has increased the prevalence and severity of toxicities. Older men and those living with frailty experience higher symptom burden associated with treatment. Although toxicities have been described in previous trials, much of the focus has been on pain and fatigue among fit and younger men, and only a few trials have examined the effects of frailty on toxicity. Thus, the aims of this study were to (1) understand the prevalence, duration, and changes in symptom severity among older men receiving mPC treatments; and (2) examine differences among frail and non-frail men. Methods: Men aged 65+ with mPC starting chemotherapy (chemo), androgen receptor-axis-targeted (ARAT) therapies, or radium-223 (Rad223) from two academic Canadian cancer centres were enrolled in a prospective cohort study. Participants self-reported symptoms daily using the Edmonton Symptom Assessment Scale (ESAS) for the first treatment cycle via internet or telephone. Frailty status was determined using the Vulnerable Elders Survey (VES-13). Study outcomes were the development of moderate-to-severe symptoms (ESAS≥4), their duration, and the proportion of participants who had improvements in symptom severity (ESAS<4) after reporting moderate-to-severe symptoms at baseline. Outcomes were determined using descriptive statistics. Associations between symptom prevalence, duration, and frailty were assessed using t-tests and chi-square tests. Results: 90 men (mean age=77 +/- 6.1 years, 58% frail (VES-13≥3)) starting chemo (n=34), an ARAT (n=43), or Rad223 (n=13) were included. The most common moderate-to-severe symptoms across cohorts were fatigue (46.8%), insomnia (42.9%), poor wellbeing (41.2%), decreased appetite (37.1%), and pain (35.9%). These symptoms were numerically higher in frail men, but differences between frail and non-frail were only statistically significant for poor wellbeing (62.5% in frail vs. 31.4% in non-frail, p=0.039). On average, poor wellbeing lasted 6.5 days (SD=7.6) days, decreased appetite lasted 5.5 days (SD=4.9), fatigue lasted 5.1 days (SD=7.3), pain lasted 4.7 days (SD=5.6), and insomnia lasted 4.6 days (SD=6.2) across cohorts. Fatigue and pain lasted numerically longer in frail men whereas insomnia, poor wellbeing, and decreased appetite lasted numerically longer in non-frail men, but these differences were not statistically significant. Among participants who reported moderate-to-severe symptoms at baseline, 15.4%, 10.7%, 7.7%, 5.3% and 3.6% had improvements in pain, appetite, wellbeing, insomnia, and fatigue, respectively. Conclusions: Understanding temporal patterns of symptoms and the impact of frailty in older men receiving mPC treatments may help inform supportive care approaches. Clinical trial information: NCT04193657 .

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)0.004
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 score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.285
GPT teacher head0.483
Teacher spread0.197 · 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".

Quick stats

Citations1
Published2023
Admission routes3
Has abstractyes

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