Name Recognition to Identify Patients of South Asian Ethnicity within the Cancer Registry
Bibliographic record
Abstract
OBJECTIVE: The goal of this project was to develop a list of forenames and surnames of South Asian (SA) women that could be used to identify SA breast cancer patients within the cancer registry. This list was compiled, evaluated, and validated to ensure comprehensiveness, accuracy, and applicability of SA names. METHODS: This project was conducted by Canadian researchers who are immersed in conducting behavioral studies with SA women diagnosed with cancer in the province of British Columbia. Recruiting SA cancer patients for research can be a difficult task due to social and cultural factors. Methods used by other researchers to identify ethnicity related unique names were employed to filter surnames and forenames that were not common to this ethnic group. Co-author (Gurpreet Oshan) of SA ethnicity rigorously identified and deleted multiple lists and redundant entries along with common English forenames which resulted in a list of 16,888 SA forenames. All co-authors of Indian ethnicity (Gurpreet Oshan, Savitri Singh-Carlson, Harajit Lail) were involved in critiquing and manually reviewing the names list throughout this process. Comprehensive lists of SA surnames and women's forenames were reviewed to identify those that were unique to SA ethnicity. Accuracy was ensured by constantly filtering the redundancy by using an Excel program which helped to illustrate the number of times each name was spelled in different ways. RESULTS: The final lists included 9112 surnames and 16,888 forenames of SA ethnicity. On the basis of the surname linkage only, the sensitivity of the list was 76.6%, specificity was 62.9%, and the positive predictive value was 58.5%. On the basis of both the surname and forename linkage, the specificity of the list was 88.6%. These lists include variations in spelling forenames and surnames as well. CONCLUSIONS: The list of surnames and forenames can be useful tools to identify SA ethnic groups from large population database in healthcare-related research. Ethnicity-specific population research is important in order to help identify how cancer care should be delivered for the SA population, as well as for planning and provision of other related health services. We are willing to share this list upon request to the authors.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".