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Longitudinal symptom burden among early phase cancer clinical trial (EP-CT) participants.

2025· article· en· W4414913409 on OpenAlexaboutno aff
Anh B. Lam, Andrea Pelletier, Sienna Durbin, Laura A. Petrillo, Rachel Jimenez, Victoria Turbini, Viola Bame, Kaitlyn Lynch, M. Boulanger, Leah L. Thompson, Cynthia Moore, Vaishnavi Yalala, Nicholas Ollila, Benjamin Malowitz, Casandra McIntyre, Dejan Juric, Debra Lundquist, Ryan David Nipp

Bibliographic record

VenueJCO Oncology Practice · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsQuality of life (healthcare)AnxietyClinical trialLongitudinal studyBaseline (sea)Depression (economics)CancerBreast cancerGastrointestinal cancer

Abstract

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412 Background: Individuals with cancer participating in EP-CTs often experience burdensome symptoms. However, little is known about their changes in symptom burden over time and how they relate to other patient-reported outcomes (PROs) and clinical outcomes. Methods: We prospectively enrolled adults with cancer participating in EP-CTs at Massachusetts General Hospital from 4/2021-1/2023. Participants completed monthly surveys throughout EP-CT that assessed symptoms (Edmonton Symptom Assessment System [ESAS]), quality of life (QOL; Functional Assessment of Cancer Therapy-General [FACT-G]), hope (Herth Hope Index), depression/anxiety symptoms (Patient Health Questionnaire 4 [PHQ4]), and financial wellbeing (COST tool, higher scores indicate greater financial wellbeing). We used linear mixed models to assess associations of symptom burden over time with baseline PROs and applied Cox regression to assess correlations of symptom scores from baseline to months 1, 2, and 3 with clinical outcomes (hospitalizations, time on trial, overall survival). Results: We enrolled 196 participants (median age = 63.3 [range: 31.8-88.6], 58% female, median time on trial = 60 days), with the most common cancer types gastrointestinal (34%) and breast (20%). We found no significant associations among patient characteristics and longitudinal ESAS scores. Higher baseline FACT-G emotional wellbeing (B = -.01, p = .015) correlated with improvement in longitudinal ESAS total scores. Higher baseline PHQ anxiety (B = .19, p = .035) correlated with worsening ESAS total scores. Higher baseline QOL (B = -.01, p < .001), FACT-G physical wellbeing (B = -.02, p = .003), financial wellbeing (B = -.01, p = .046), and hope (B = -.03, p < .001) scores were associated with improvement in ESAS psychological scores over time. Higher baseline PHQ depression (B = .86, p = .001) and anxiety (B = .09, p < .001) were associated with worsening ESAS psychological scores over time. The table displays associations of changes in ESAS from baseline to months 1, 2, and 3 with clinical outcomes; findings demonstrate that worsening ESAS scores from baseline to month 2 correlated with higher hospitalization risk, shorter time on trial, and worse survival. Conclusions: In this study of EP-CT participants, we found associations of baseline QOL, financial wellbeing, hope, and anxiety with symptom burden over time as well as associations between change in symptom burden and clinical outcomes. Changes from ESAS baseline to month 2 correlated with higher healthcare use, shorter time on trial, and worse survival. Future efforts to enhance EP-CT participants’ outcomes should seek to understand the underlying mechanisms of these findings. Longitudinal ESAS Hospitalization Time on Trial Survival HR P HR P HR P Baseline to Month 1 (n=171) 1.02 .106 1.01 .193 1.01 .219 Baseline to Month 2 (n=129) 1.03 .014 1.02 .017 1.02 .025 Baseline to Month 3 (n=86) 1.00 .841 1.02 .048 1.01 .364

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)0.038
metaresearch head score (Gemma)0.038
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.640
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0380.038
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.003

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 head0.641
GPT teacher head0.628
Teacher spread0.013 · 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; both teacher heads agree on what is shown here.

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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