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Patient-reported hope, quality of life, symptom burden, coping, and financial toxicity in early-phase clinical trial participants.

2022· article· en· W4298139506 on OpenAlexaboutno aff
Debra Lundquist, Andrea Pelletier, Sienna Durbin, Viola Bame, Victoria Turbini, Kaitlyn Lynch, Andrew Johnson, Hope Heldreth, Megan Healy, Casandra McIntyre, Dejan Juric, Rachel Jimenez, Betty Ferrell, Ryan David Nipp

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineQuality of life (healthcare)Coping (psychology)Clinical trialDenialInternal medicinePhysical therapyClinical psychologyNursing

Abstract

fetched live from OpenAlex

275 Background: Early phase clinical trials (EP-CTs) investigate novel treatment options in oncology, with recent advances in personalized therapy leading to improved outcomes and offering hope to patients with cancer. However, little research has sought to understand associations of patient-reported hope with quality of life (QOL), symptom burden, coping, and financial toxicity in EP-CT participants. Methods: We prospectively enrolled consecutive adults with cancer participating in EP-CTs at Massachusetts General Hospital from 04/2021-05/2022. Participants completed baseline surveys prior to treatment initiation that assessed hope (Herth Hope Index [HHI], higher scores indicate greater hope), QOL (Functional Assessment of Cancer Therapy-General), symptom burden (physical: Edmonton Symptom Assessment System [ESAS]; psychological: Patient Health Questionaire-4 [PHQ4]), coping (Brief COPE: self-blame, acceptance, denial, emotional support, active, behavioral disengagement), and financial toxicity (COST tool, higher scores indicate greater financial wellbeing). We used regression models to determine associations of hope scores with patient-reported QOL, symptom burden, coping, and financial toxicity. Results: Of 157 eligible patients, we enrolled 129 (enrollment rate 82.2%, median age = 62.5 years [range 33.0-83.0], 53.9% female, and 96.0% metastatic cancer). Most common cancer types were gastrointestinal (37.5%), breast (20.3%), lung (8.6%), and head and neck (7.8%). Patients had an average HHI score of 27.5 (range 15.3 – 36.0), with 30.5% reporting high levels of hope. We found associations of higher hope scores with better QOL (B = 0.24, p < 0.001) and lower symptom burden (ESAS-physical: B = -0.14, p < 0.001; PHQ4-depression: B = -2.07, p < 0.001; PHQ4-anxiety: B = -0.93, p = 0.001). We also found that hope scores were associated with patients’ coping (self-blame [B = -1.44, p < 0.001]; acceptance [B = 1.40, p < 0.001], denial [B = -1.12, p = 0.004], emotional support [B = 0.99, p < 0.001], active [B = 1.02. p = 0.001], behavioral disengagement [B = -2.52, p < 0.001]). Lastly, we found that higher hope scores were associated with greater financial wellbeing (B = 0.11, p = 0.026). Conclusions: In this prospective cohort study, we demonstrated a substantial proportion of EP-CT participants had high baseline hope and identified associations of hope scores with other important patient-reported outcomes. Specifically, we found novel associations of higher hope scores with better QOL, lower symptom burden, more adaptive coping mechanisms, and greater financial wellbeing, underscoring the importance of targeting these patient-reported outcomes when seeking to enhance the care experience of EP-CT participants.

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)0.025
metaresearch head score (Gemma)0.018
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 score0.130

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.297
GPT teacher head0.474
Teacher spread0.177 · 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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Citations2
Published2022
Admission routes1
Has abstractyes

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