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Illness perceptions, financial toxicity, symptom burden, and survival in cancer clinical trial (CCT) participants.

2021· article· en· W3199015518 on OpenAlexaboutno aff
Subha Perni, Chukwuma Azoba, Emily Gorton, Elyse R. Park, Bruce A. Chabner, Beverly Moy, Ryan David Nipp

Bibliographic record

VenueJournal of Clinical Oncology · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineQuality of life (healthcare)Marital statusClinical trialDiseaseInternal medicinePopulationEnvironmental health

Abstract

fetched live from OpenAlex

160 Background: Patients’ perceptions of their illness are important for treatment decision-making and quality of life. Limited data exist describing associations of illness perceptions with other patient-centered outcomes, particularly in CCT participants. We sought to examine associations among illness perceptions and CCT patients’ financial toxicity, physical and psychological symptoms, and survival. Methods: From 7/2015-7/2017, we prospectively enrolled CCT participants who expressed interest in financial assistance programs (n = 157) and a group of patients matched by age, sex, cancer type, specific trial, and trial phase (n = 103). We assessed baseline illness perceptions (Brief Illness Perceptions Questionnaire [BIPQ] with scores > 50 indicating negative perceptions), financial toxicity (degree costs of cancer care have been a burden, moderate to catastrophic indicating financial toxicity), physical (Edmonton Symptom Assessment Scale [ESAS]) and psychological (Patient Health Questionnaire-4 [PHQ-4]) symptoms. We used descriptive statistics to examine associations of BIPQ and sociodemographic/clinical factors, financial toxicity, ESAS, PHQ-4, and overall survival. We used the Kaplan-Meier method to estimate median survival times and Cox regression to assess the association of BIPQ and overall survival. Results: Among 260 patients, 189 (72.7%) completed BIPQ surveys (median age 69 [Range 26 to 83] years, 66.1% female). 68.8% had negative illness perceptions. We found no significant associations among negative illness perceptions and patients’ age, sex, race, education, marital status, performance status, insurance, cancer type, metastatic disease status, self-reported income, trial phase, trial year, or Charlson Comorbidity Index score. Patients with negative illness perceptions were more likely to report financial toxicity (69.8% vs 48.8%, p = 0.006), and had higher ESAS-total (Medians: 44 [Range 0-89] vs 21 [Range 0-78], p < 0.001), PHQ-4 depression (Medians: 2 [Range 0-6] vs 0 [Range 0-6], p < 0.001), and PHQ-4 anxiety (Medians: 3 [Range 0-6] vs 1 [Range 0-6], p < 0.001) scores. Patients with negative illness perceptions had shorter overall survival (Medians: 22 [Range 10-29] vs 42 [Range 28-Not Reached] months, log-rank p = 0.004). Adjusting for receipt of financial assistance, patients with negative illness perceptions experienced higher risk of death (HR 1.65, 95% CI 1.10-2.48). Conclusions: In this prospective study of CCT participants, we found that patients with negative illness perceptions experienced greater financial toxicity, more symptom burden, and worse survival than those with more positive perceptions, despite comparable sociodemographic/clinical factors. These findings highlight the need to assess and address patients’ illness perceptions and financial burden when seeking to enhance patient-centered outcomes in oncology.

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)0.003
metaresearch head score (Gemma)0.009
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 score0.003
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.265
GPT teacher head0.471
Teacher spread0.206 · 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
Published2021
Admission routes1
Has abstractyes

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