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Timing of receipt of palliative care among participants in early-phase cancer clinical trials.

2023· article· en· W4388199174 on OpenAlexaboutno aff
Sienna Durbin, Andrea Pelletier, Laura A. Petrillo, Rachel Jimenez, Janice Kim, Victoria Turbini, Kaitlyn Lynch, Allison M. Kehlmann, Nicholas Chevalier, Viola Bame, Hope Heldreth, J. A. P. da Silva, Casandra McIntyre, Dejan Juric, Debra Lundquist, Ryan David Nipp

Bibliographic record

VenueJCO Oncology Practice · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineReferralQuality of life (healthcare)ReceiptPalliative careClinical trialPerformance statusCancerFamily medicineInternal medicinePhysical therapyNursing

Abstract

fetched live from OpenAlex

260 Background: Early phase clinical trials (EP-CTs) investigate novel therapeutics for patients with cancer. EP-CT participants often have advanced stages of disease, have received multiple lines of prior therapies, and remain sufficiently functional to enroll on EP-CTs. Limited research exists to describe the use and timing of palliative care (PC) in EP-CTs. Methods: We conducted a prospective study of patients with cancer enrolled on EP-CTs at Massachusetts General Hospital from April 2021- January 2023. We extracted demographic and clinical characteristics from the electronic health record as well as timing of receipt of PC (before/during EP-CT vs after EP-CT/never) and reason for PC referral documented in the referral. We used patient-reported surveys at time of treatment initiation to assess symptom burden (Edmonton Symptom Assessment System [ESAS]), financial toxicity (Comprehensive Score for Financial Toxicity [COST], lower scores indicate greater toxicity), quality of life (QOL; Functional Assessment of Cancer Therapy-General [FACT-G]), and hope (Herth Hope Index [HHI], higher scores indicate greater hope). We used descriptive statistics to explore associations of timing of receipt of PC with patient characteristics, symptom burden, financial wellbeing, QOL, and hope. Results: Of 221 eligible patients, we enrolled 204 (enrollment rate 92.3%, median age=63.4 years [range 54.8-70.2]; 57.0% female, 94.1% metastatic cancer). Less than one third of patients received PC before/during EP-CT (31.8% before/during, 68.2% post/never). Reasons for referral include: symptom management (84.4%), coping (12.5%), advance care planning (7.8%), illness understanding (1.6%), and treatment decision-making (1.6%). Patients age <65 were more likely than those 65+ to receive PC before/during EP-CT (39.1% vs 22.1%, p=0.01). Patients with ECOG=1 were more likely than those with ECOG=0 to receive PC before/during EP-CT enrollment than post enrollment/never (37.9% vs 22.9%, p=0.01). Patients who received PC before/during EP-CT had a higher symptom burden (ESAS: 20.7 vs 15.5, p=0.01), lower QOL (70.9 vs 77.0, p=0.003), and worse financial toxicity (25.2 vs 29.4, p=0.003). We found no significant differences in hope scores based on timing of receipt of PC (27.6 vs 27.1, p=0.53). Conclusions: In this prospective cohort of EP-CT participants with cancer,less than one third received PC before/during EP-CT enrollment. Symptom management was the most common reason for referral to PC, which aligns with our patient-reported survey data. Characteristics associated with receiving PC before/during trial participation include age, decreased performance status, higher symptom burden, lower QOL, and increased financial toxicity. These findings suggest that earlier PC was appropriately delivered to those most in need yet highlight the importance of efforts to ensure earlier integration of PC in this population.

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)0.007
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.424
GPT teacher head0.527
Teacher spread0.103 · 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 teacher head, not a consensus.

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

Citations0
Published2023
Admission routes1
Has abstractyes

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