ArticleGynecologic oncology reports2026
A novel surname-based methodology to improve the disaggregation of Asian American ethnic subgroups in patients with ovarian cancer.
Article in Gynecologic oncology reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Over the last 30 years, only two-thirds of gynecologic oncology publications reported on race/ethnicity. Demographic reporting of disaggregated racial/ethnic groups can elucidate disparities and elicit unique insights into care. We aimed to improve the accuracy of race and ethnicity reporting in patients with ovarian cancer by integrating self-reported data with a novel surname-based methodology. Methods: Patients with ovarian cancer between 2011 and 2023 were identified from our tumor registry. Self-reported race/ethnicity data were extracted from electronic health records. A validated surname list derived from Social Security Administration files was used to disaggregate Asian American patients and those from Other/Unknown races into the following ethnic subgroups: Chinese, Filipino, Indian, Japanese, Korean, and Vietnamese. The effectiveness of ethnic subgroup disaggregation was assessed among Asian American patients. Results: Among 813 patients identified, the self-reported racial/ethnic distribution was: 10% (n = 81) Asian, 12% (n = 96) Hispanic, 1% (n = 7) Multiracial, <1% (n = 1) non-Hispanic Native Hawaiian or Pacific Islander, 8% (n = 61) non-Hispanic Black, and 64% (n = 520) non-Hispanic White. Of the 6% (n = 47) of patients of Other/Unknown race, surname analysis reclassified 5 (11%) patients as Asian American. Among the 89 self-identified Asian American patients, surname analysis disaggregated more patients (74%, n = 66) into ethnic subgroups than self-reported race/ethnicity (61%, n = 55). Combining both methodologies enabled the disaggregation of 91% (n = 81) of Asian American patients. Concordance between surname-based and self-reported methods was high (Spearman's Rho = 0.84). Conclusion: Integrating self-reported race/ethnicity with surname-based analysis improved the accuracy of racial classification and Asian American ethnic subgroup disaggregation. Leveraging both approaches can enhance the granularity and accuracy of demographic reporting.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.