Assessing racial differences in North American hereditary hemorrhagic telangiectasia study recruitment and care
Bibliographic record
Abstract
BACKGROUND: There is increasing evidence of health outcome disparities due to inequitable healthcare. These inequities are likely compounded in rare disease care and research. We aimed to identify disparities in access to clinical care and research for patients with hereditary hemorrhagic telangiectasia (HHT) in North America. METHODS: We collected race data from the Toronto HHT Centre Brain Vascular Malformation Consortium (BVMC) recruits, the UCSF HHT Centre BVMC recruits, and the UCSF HHT Centre clinic patients, and compared proportions to local populations (2016 Canadian Census and 2010 San Francisco Bay Area Census). RESULTS: At the UCSF HHT center, there was a significant association between race and BVMC enrollment status (p = 0.033). The proportion of White BVMC recruits was significantly higher than reported in the SF Bay Area Census data (p < 0.001). At the Toronto HHT centre the proportion of White BVMC recruits was significantly higher than reported in the Canadian Census provincial data (p < 0.001), and Toronto Metropolitan Area data (p < 0.001). CONCLUSION: We report preliminary evidence of racial differences in access to HHT care and research in North America. Our findings indicate a need for race data collection and reporting, as well as identification of barriers and potential solutions in HHT care and research.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".