Pain severity among Black and White patients with cancer within a remote symptom monitoring program using electronic patient-reported outcomes.
Bibliographic record
Abstract
138 Background: Black patients with cancer report higher pain intensity for both consistent and breakthrough pain. Remote symptom monitoring (RSM) using electronic patient-reported outcomes (ePROs) allows patients with cancer to communicate symptoms between visits. This study evaluated whether there were racial disparities in pain severity reporting among Black and White patients with cancer utilizing RSM with ePROs. Methods: This retrospective study analyzed data from 1453 patients with cancer undergoing treatment who utilized an RSM platform for the completion of weekly PRO-CTCAEs surveys to report pain intensity. Survey data from patients during the initial 6 months following RSM enrollment at the University of Alabama at Birmingham (UAB) and the Mitchell Cancer Institute (MCI) were included. Descriptive statistics were compared using frequencies, percentages, and Cramer’s V for categorical variables. Regression models were adjusted for age, sex, cancer type, and rurality. Results: Data from 1453 patients and 17,722 surveys were analyzed. 454 (31.2%) patients were Black and 999 (68.8%) patients were White. Among 454 baseline surveys completed by Black patients, no pain was reported in 68%, mild pain in 4%, moderate in 12%, and severe in 16%. In comparison, among 999 baseline surveys completed by White patients, no pain was reported in 71%, mild pain in 3%, moderate in 12% and severe in 14% (V=0.03). Among all post-baseline surveys completed by Black patients, no pain was reported in 69% (n=5081), mild pain in 5%, moderate in 13% and severe in 13%. In comparison, among all post-baseline surveys completed by White patients, no pain was reported in 72% (n=11188), mild pain in 4%, moderate in 14% and severe in 10% (V=0.05). In adjusted analysis, severe pain at follow-up was higher amongst Black patients than White patients (OR 1.17; 95% CI 0.87-1.56). Conclusions: Although modestly increased prevalence (3%) in any pain at baseline and severe pain during their 6 months after enrollment in RSM was observed for Black participants compared to Whites, this finding was not significant in adjusted models. This analysis suggests similar pain levels for Black and White patients within RSM programs. RSM can promote proactive reporting and early intervention for pain, which may help to mitigate previously reported disparities in long-term pain for Black individuals.
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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 | 0.003 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".