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Identifying multi-level social determinants for disparities in survival and patient-reported outcomes in national head and neck cancer trials.

2025· article· en· W4410810832 on OpenAlexaff
Jinbing Bai, Mónica Guerra, F. Nguyen-Tân, David I. Rosenthal, Jimmy J. Caudell, Maura L. Gillison, Loren K. Mell, Deborah Watkins Bruner, Katherine A. Yeager, Ronald C. Eldridge, Wade L. Thorstad, Mary Jue Xu, Sara Medek, Michelle Echevarria, Musaddiq Awan, Dong M. Shin, Stephanie L. Pugh, Sue S. Yom

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsMedicineHead and neck cancerCancerHead and neckOncologyInternal medicineFamily medicineGerontologySurgery

Abstract

fetched live from OpenAlex

11066 Background: This study aimed to determine to what extent area-level social determinants of health (SDOH) interact with individual, institutional, and biological factors to predict outcomes in head and neck cancer (HNC) trials. Methods: Five NRG Oncology HNC trials (2635 patients receiving chemoradiation) were analyzed. Area-level SDOH coded by patient ZIP codes included rurality (rural-urban commuting area code), neighborhood socioeconomic deprivation (Area Deprivation Index [ADI] categorized as upper vs. lower quartile), and travel burden (distance and time to treatment site). Individual (demographic, cancer and treatment-related factors), institutional (accrual volume), biological (HPV+/-) factors, and outcomes (overall survival [OS], progression free survival [PFS], quality of life [QOL], and symptoms) were analyzed. Multivariable Cox proportional hazards regression and mediation analysis using logistic regression assessed associations using hazard ratio (HR) or odds ratios (OR) and 95% confidence intervals (CI). Results: Most patients were White (88%), non-Hispanic (92.7%), of mean age of 57 years, HPV+ (64.6%), and received intensity-modulated radiotherapy (95.9%) and cisplatin (94.2%). ADI and rurality were not associated with OS and PFS. OS and PFS were higher in patients with travel time <1 hour (HR=0.85, 95% CI [0.75, 0.98]; HR=0.85, 95% CI [0.73, 0.98]) and travel distance <50 miles (HR=0.84, 95% CI [0.72, 0.96]; HR=0.85, 95% CI [0.73, 0.98]). ADI, travel time, and travel distance were not associated with QOL decline. Patients treated at institutions with high rural accrual volume had worse QOL decline from baseline (OR=0.36, 95% CI [0.15, 0.85]). The impact of travel distance but not time varied by race to influence QOL decline (OR=0.38, 95% CI [0.16, 0.93]). ADI was not associated with symptoms, but patients from institutions with high rural accrual volume had worse symptoms (OR=7.83, 95% CI [1.98, 31.01]). HPV status had a significant indirect effect on the relationship between travel distance and survival at 1 year (estimate [β]=0.03, 95% CI [0.01, 0.05]) and 5 years (β=0.03, 95% CI [0.004, 0.05]), as well as a direct and total mediation effect of travel distance on QOL decline at 1 year (direct β=-0.08, 95% CI [-0.16, -0.004]; total β=-0.89, 95% CI [-0.17, -0.01]). Conclusions: This study showed the impact of area-level SDOH and their interactions with race and institutional accrual volume, which are associated with survival, QOL, and symptom changes. HPV status potentially mediated the effects of travel distance on outcomes. Our findings provide novel approaches to identify patients at risk for poor outcomes, such as those with travel burden at institutions with high rural accrual, to design community-based interventions to improve cancer outcomes.

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)0.028
metaresearch head score (Gemma)0.034
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 score0.028
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.034
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.853
GPT teacher head0.733
Teacher spread0.121 · 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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