Abstract C003: Sociodemographic and clinical characteristics associated with worst pain intensity among cancer patients
Bibliographic record
Abstract
Abstract Introduction: Approximately 70% of individuals living with cancer experience persistent pain. Previous studies showed racial/ethnic differences existing across various cancer-related outcomes. Yet, few studies have examined the racial/ethnic differences in worst pain intensity among cancer patients. Thus, the goal of this secondary data analysis was to identify predictors of worst pain intensity, including race/ethnicity, and cancer stage in a diverse sample. Methodology: A convenience sample of cancer patients (N=1,516) recruited from cancer centers in the Western and Midwestern United States completed questionnaires collecting demographic, chronic pain, and cancer-specific information. In addition to race and ethnicity, covariates for the linear regression included: other demographic characteristics, tumor stage, cancer type, cancer stage, and substance use. The study outcome, worst pain intensity, was measured on 0 (no pain) to 10 (worst pain) scale and was captured using a validated modified McGill Pain Questionnaire (PAINReportIt). A multinomial generalized linear regression model was utilized to determine associations between selected predictors and pain intensity. Statistical significance was considered at p< .05. Results: Our study sample was predominantly White (65.0%), Black (24.1%), and Other (10.9%). On average, participants were 58.9 (SD=14.1) years old. Additionally, participants reported a 5.9 (SD=3.0) worst pain intensity score. Selected significant covariates: being non-Hispanic Black (β=0.67; P= 0.002), belonging to an Other racial group (β= 1.04; P= 0.0004), earning less than $10,000 annually (β=0.77; P= 0.0151), earning between $10,000 - $50,000 annually (β= 0.85; P= 0.0038), having toothache pain (β=0.12; P= 0.0004), and having stage 4 cancer (β=0.82; P= 0.0007) were positively associated with worse cancer pain. Conclusion: Our analysis suggests that being non-Hispanic Black, a member of an other racial group, low socioeconomic status, having had toothache pain, and having advanced stage cancer are significant predictors of worst pain intensity among cancer patients. Future studies focused on the management of cancer-related pain should target under-resourced individuals and those with advanced cancer for pain prevention strategies to prevent the escalation of pain intensity. Additionally, future studies should continue to oversample underrepresented Black populations in order to continue assessing disparities in clinical cancer outcomes. Citation Format: Verlin Joseph, Keesha Roach, Staja Booker, Jinhai Huo, Yingwei Yao, Xinguang Chen, Robert L Cook, Diana J Wilkie. Sociodemographic and clinical characteristics associated with worst pain intensity among cancer patients [abstract]. In: Proceedings of the Twelfth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2019 Sep 20-23; San Francisco, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl_2):Abstract nr C003.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".