The authors respond
Bibliographic record
Abstract
We thank Dr. Grant for his comments regarding our recent article on the role of hypertension and chronic kidney disease in the racial disparities in the incidence of renal cell carcinoma among members of Kaiser Permanente Northern California, a large integrated health care system in the greater San Francisco Bay area.1 Dr. Grant presents intriguing evidence to suggest that racial differences in circulating 25-hydroxyvitamin D [25(OH)D] levels may contribute to the disparities in renal cell carcinoma, and this hypothesis warrants further examination. However, we note that the findings of studies evaluating the relation between circulating 25(OH)D levels and renal cell carcinoma risk have been inconsistent. In contrast to the more recent report from the EPIC cohort,2 no association was observed in a prospective investigation of renal cell carcinoma in the NCI Cohort Consortium that included a larger number of cases (560 and 775 cases, respectively).3 Future studies evaluating the association between circulating 25(OH)D levels and risk of renal cell carcinoma among blacks and other non-white populations would be informative. Beyond circulating 25(OH)D levels, several other factors might also explain how hypertension and chronic kidney disease contribute to racial disparities in the overall burden of renal cell carcinoma including differences by race in hypertension control and management of chronic kidney disease, the prevalence of modifiable risk factors related to renal cell carcinoma (e.g., obesity, smoking), and/or genetic susceptibility. Further investigation of each of these factors will likely yield important insights into the underlying mechanisms through which hypertension and chronic kidney disease influence renal cell carcinoma risk and will help us to better understand the racial disparities in this malignancy. Jonathan N. Hofmann Occupational and Environmental Epidemiology Branch Division of Cancer Epidemiology and Genetics National Cancer Institute Bethesda, MD [email protected] Douglas A. Corley Division of Research Kaiser Permanente Northern California Oakland, CA Joanne S. Colt Division of Cancer Epidemiology and Genetics National Cancer Institute Bethesda, MD Brian Shuch Department of Urology Yale School of Medicine New Haven, CT Wong-Ho Chow Department of Epidemiology The University of Texas MD Anderson Cancer Center Houston, TX Mark P. Purdue Division of Cancer Epidemiology and Genetics National Cancer Institute Bethesda, MD Ontario Institute for Cancer Research Toronto, ON Canada
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.447 | 0.327 |
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".