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A nurse-led intervention to enhance quality of life (QOL) among early phase cancer clinical trial participants.

2025· article· en· W4414893290 on OpenAlexaboutno aff
Debra Lundquist, Nora Horick, Laura A. Petrillo, Rachel Jimenez, Victoria Turbini, Cynthia Moore, Allison E. White, Leah L. Thompson, Anh B. Lam, M. Boulanger, Viola Bame, Kaitlyn Lynch, Casandra McIntyre, Dejan Juric, Betty Ferrell, 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
FundersOncology Nursing Foundation
KeywordsQuality of life (healthcare)Intervention (counseling)Palliative careCoping (psychology)Clinical trialSocial supportClinical endpointRandomized controlled trial

Abstract

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336 Background: Efforts to address supportive care (SC) concerns among EPCT participants are key for improving QOL and decreasing symptom burden. Clinical Research Nurses (CRNs) are uniquely positioned to address SC concerns among EPCT participants, and thus we sought to determine feasibility and acceptability of a nurse-led intervention in this population. Methods: We conducted an open pilot study, prospectively enrolling EPCT participants, CRNs, and collaborative team clinicians (clinicians) from palliative care, social work, and psychology at Massachusetts General Hospital from 7/2023-09/2024. The intervention included three structured CRN visits with EPCT participants and implementation of bi-monthly collaborative team meetings. Participants completed patient-reported outcomes (PROs) at enrollment and three monthly timepoints: QOL (Functional Assessment of Cancer Therapy-General [FACT-G]), hope (Herth Hope Index), symptoms (Edmonton Symptom Assessment System), and coping (Brief-Cope). We assessed CRN and clinician acceptability (Acceptability of Intervention Measure [AIM]). Exit interviews with CRNs and clinicians assessed perceptions of usefulness, effectiveness, relevance. The primary endpoint was feasibility, defined as ≥ 60% enrollment of patients approached and > 70% of participants completing ≥ 60% of PROs. Results: We enrolled 50/61 patients approached (enrollment rate: 85%; median age: 58.7 years [IQR: 52.8-65.7 years], 60% female, 94% metastatic cancer, ECOG 0=38% and ECOG 1=62%). Most common cancers were gastrointestinal (22%) and breast (7%). At enrollment, 24% (n=12) reported bill paying concerns, 54% had seen a social worker (n=27), and 24% (n=12) had seen palliative care. Median time on trial (TOT) was 3.4 months. Taking TOT in account, 90% (n=45) completed ≥ 60% of PROs. Changes in mean PRO scores from baseline to month 3 showed improvement over time: FACT-G total (+5.8, p<0.01), FACT-G subscales: physical (+1.7, p=0.04), social (+1.3, p=0.04), emotional (+1.8, p=0.02). Among 10 CRNs and 10 clinicians approached, all participated, and acceptability of the intervention was demonstrated by high AIM scores (mean=17.6; scale range: 4-20) and exit interviews (themes identified: appreciated patient-centric collaboration, logistically possible and meaningful, unexpected positive outcomes). Conclusions: In this pilot study, we demonstrated feasibility and acceptability of an innovative nurse-led intervention to address SC concerns among EPCT participants. Notably, we found promising results for improved PROs over time and favorable exit interview feedback, which underscore the intervention’s potential usefulness, effectiveness, and relevance. Findings support the need for randomized trials to assess efficacy of this intervention.

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)0.006
metaresearch head score (Gemma)0.010
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 score0.007
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.594
GPT teacher head0.669
Teacher spread0.075 · 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
Published2025
Admission routes1
Has abstractyes

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