Abstract B77: Ethnicity and hematopoietic stem cell transplantation (HSCT) outcomes in British Columbia, Canada
Bibliographic record
Abstract
Abstract Purpose: There are documented ethnic disparities in cancer care access, use and clinical outcomes in North America; however, the role of ethnicity on different outcomes of unrelated donor hematopoietic stem cell transplantation (HSCT) as an established treatment for many hematological and non-hematological malignancies has not been studied for Canadian patients. Patients and Methods: We reviewed the registry data of 395 patients receiving first time unrelated donor HSCT for hematological malignancies at leukemia/BMT center of British Columbia (BC) between 1988 and 2008. They were reported to be white (N=340), Asian (N=32), native (N=8), Hispanic (N=3), black (N=2), mixed (N=9) and other- not specified (N=1) which were further categorized as white (N=340) and non-white (N=55). Different HSCT outcomes were compared by log-rank test and Cox proportional hazard regression adjusting for significant patient, disease and transplant related factors at 95% significance level. Results: Univariate and multivariate analysis didn't show any statistically significant difference for overall survival, disease-free survival, relapse, acute graft versus host disease (aGVHD) grade 2+ and chronic graft versus host disease (cGVHD) rates between whites and non-whites. We reanalyzed a subset of 115 cases (88 whites and 27 non-whites) who received their HSCT after June 2001 (start date for application of high resolution DNA-based HLA matching in the study center) and their underlying diagnosis was acute myeloid leukemia, acute lymphoid leukemia, chronic myeloid leukemia and myelodysplastic syndrome. The results showed a higher risk of cGVHD for non-whites in univariate analysis (HR1.82, 95% CI1.07-3.12, P=0.03), however; adjusting for covariates in the multivariate analysis resolved this difference (P = 0.09). Conclusion: According to our data, HSCT clinical outcomes are comparable between white and non-white ethnic minorities in BC. The contrasting result with that of US studies might be due to different ethnic composition of BC and using a heterogeneous non-white ethnic group as the comparator (to increase the power of study) which could mask any unsought differences in the subgroups. Citation Information: Cancer Epidemiol Biomarkers Prev 2010;19(10 Suppl):B77.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".