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Comparison of symptom clusters between Black and White patients with cancer within an electronic patient-reported outcome remote symptom monitoring program.

2024· article· en· W4402965706 on OpenAlexaff
Sandra C. Olisakwe, Luqin Deng, Jeffrey Franks, Nicole E. Caston, Courtney Williams, Andrés Azuero, Chelsea McGowen, Bryanna Diaz, Carrie C. McNair, Sheila McElhany, D’Ambra Dent, Noon Eltoum, Katherine Parks, Joud El Dick, Bryan J. Weiner, Doris Howell, Angela M. Stover, Ethan Basch, Jennifer Young Pierce, Gabrielle B. Rocque

Bibliographic record

VenueJCO Oncology Practice · 2024
Typearticle
Languageen
FieldPsychology
TopicMental Health Research Topics
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsWhite (mutation)MedicineCancerPatient-reported outcomeInternal medicineQuality of life (healthcare)Biology

Abstract

fetched live from OpenAlex

328 Background: Disparities in pain management for Black patients with cancer are well-documented, but less is known about other symptom clusters like gastrointestinal (GI), respiratory, and urinary symptoms. This study assessed racial disparities in electronically reported symptom clusters among Black and White patients with cancer enrolled in a Remote Symptom Monitoring (RSM) program. Methods: Patients with cancer at the University of Alabama at Birmingham (UAB) and the Mitchell Cancer Institute (MCI) reported symptoms weekly using the Patient Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAEs). The GI symptom cluster included decreased appetite, nausea, vomiting, constipation, and diarrhea; the respiratory cluster included cough and shortness of breath; the urinary cluster included frequent urinary problems. We assessed the presence of any moderate/severe symptoms or any severe symptoms (triggering a nurse alert) in the clusters. The study evaluated surveys submitted within the first six months after RSM enrollment. Generalized linear mixed effects modeling with random effects adjusting for cancer type, sex, and age were used to calculate the odds of reporting any moderate/severe pain between Black and White patients. Results: Among 1454 patients (31% Black, 69% White), 17,937 surveys were analyzed. The median age for Black patients was 59 (IQR 47-66) and 63 (IQR 54-71) for White patients. Breast cancer was most common in both groups (36% Black, 23% White), followed by gynecological (20% Black, 17% White) and gastrointestinal cancers (19% Black, 18% White). At baseline, both races reported similar proportions of moderate/severe or severe symptom for all clusters (Table). During the first six months in the RSM program, symptom proportions remained similar (Table). These findings were consistent in adjusted analysis for the GI (OR 0.85; 95% CI 0.68-1.07), respiratory (OR 1.18; 95% CI 0.87-1.61), and urinary clusters (OR 0.92; 95% CI 0.61-1.39). Conclusions: Our findings suggest no significant racial disparities in reporting moderate/severe or severe symptoms among Black and White patients with cancer at baseline or within their initial six months in the RSM program across GI, respiratory, and urinary symptom clusters. Survey characteristics. Total Surveys(N=17,937) Baseline Surveys 6 Months Surveys White (n=999) Black(n=455) White(n=11,344) Black(n=5139) Gastrointestinal Symptoms, No. (%) Severe 1376 (8) 121 (12) 59 (13) 783 (7) 413 (8) Moderate/Severe 4701 (26) 300 (30) 130 (29) 2971 (26) 1300 (25) Respiratory Symptoms, No. (%) Severe 438 (2) 28 (3) 29 (6) 241 (2) 140 (3) Moderate/Severe 1612 (9) 90 (9) 62 (14) 996 (8) 464 (9) Urinary Symptoms, No. (%) Severe 333 (2) 31 (3) 13 (3) 184 (2) 105 (2) Moderate/Severe 620 (3) 44 (4) 21 (5) 372 (3) 183 (4)

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.157
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.111
GPT teacher head0.523
Teacher spread0.412 · 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.

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
Published2024
Admission routes1
Has abstractyes

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