Sex and Race Disparities in Hepatocellular Carcinoma Surveillance in Patients With Chronic Hepatitis B During COVID-19: A Single-Center Retrospective Review
Bibliographic record
Abstract
Background: The management of patients with chronic hepatitis B (CHB) is complex and spans multiple medical specialties. As a result of this complexity, patients with CHB often do not receive adequate monitoring including hepatocellular carcinoma (HCC) surveillance with abdominal ultrasonography. Previous studies have identified multiple factors associated with decreased HCC surveillance. We aimed to identify the impact of race and sex on HCC surveillance in patients with CHB. Methods: We performed a single health system chart review between January 2018 and January 2022. Differences between sex and race were evaluated using the Chi-square test and Fisher's exact test, and continuous variables were analyzed using analysis of variance (ANOVA). Results: A total of 248 patient records between January 2018 and January 2022 were evaluated. In total 37% of females were adequately screened for HCC in any of the 6-month time frames compared to 26% of males. During the coronavirus disease 2019 (COVID-19) surge, surveillance rates were reduced in both men and women. During the first 6 months of the COVID-19 surge, there was a significant difference in screening between men and women (19% vs. 35%, P = 0.026). There was a decrease in HCC screening across all races during the COVID-19 surge; however, no significant difference when comparing races was found. Conclusion: Men received less HCC surveillance compared to women. These differences were more pronounced during the COVID-19 pandemic surge. Obtaining appropriate surveillance is important and retrospective evaluations can help us determine the presence of health-related social needs so that progress can be made toward achieving health equity.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".