Bibliographic record
Abstract
To the Editor: We thank Paras Karmacharya and colleagues for their letter to the editor1, which furthers the discussion about racial differences in patients with ankylosing spondylitis (AS). They highlight that the reported prevalence of AS in African Americans of 8% in our study is low2. At present the true prevalence of AS in African Americans in the USA is unknown. Based on the US National Health and Nutrition Examination Survey 2009–2010 survey, the overall prevalence estimates of axial spondyloarthritis (axSpA) using the European Spondylarthropathy Study Group criteria is 0.9% in non-Hispanic blacks between the ages of 20 and 69 years3,4, but Karmacharya and colleagues acknowledged that estimates could be unreliable. The number of African Americans was too low to make any definite estimates. We acknowledge that healthcare databases have substantial variation in estimates of prevalence and incidence of chronic conditions, which limits their interpretability and utility. The true prevalence of AS in African Americans may have been underestimated … Address correspondence to Dr. D.K. Singh, Case Western Reserve University, MetroHealth Medical Center, Division of Rheumatology, 2500 MetroHealth Drive, Cleveland, Ohio 44109, USA. E-mail: dilpreetsinghmd{at}gmail.com
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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.028 | 0.030 |
| Insufficient payload (model declined to judge) | 0.008 | 0.009 |
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".