True Difference or Detection Bias: Racial Differences in Clinical Features and Comorbidities in Ankylosing Spondylitis in the United States
Bibliographic record
Abstract
To the Editor: We read with interest about the study by Singh and Magrey (“Racial differences in clinical features and comorbidities in ankylosing spondylitis in the United States”)1. The authors selected patients with ankylosing spondylitis (AS) from a large clinical informatics tool, the Explorys platform, with multiple participating healthcare organizations, which increases the generalizability of the findings. To increase the validity of the cases, Singh and Magrey included AS patients with at least 2 visits with a rheumatologist. While this approach may increase case validity, it might miss patients who have not seen a rheumatologist or were lost to followup after the initial visit. Only 8% of the patients included in the study were African American. It is unclear whether such a low prevalence is from low detection in these patients or due … Address correspondence to P. Karmacharya, Division of Rheumatology, Mayo Clinic College of Medicine, 200 First St. S.W., Rochester, Minnesota 55905, USA. E-mail: paraskarmacharya{at}gmail.com
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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 | 0.023 | 0.180 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".