A Response to: Letter to the Editor Regarding Impact of Race on the Efficacy and Safety of Tofacitinib in Rheumatoid Arthritis: Post Hoc Analysis of Pooled Clinical Trials
Bibliographic record
Abstract
in the article introduction and in the discussion of our results [2].Thus, there is no disagreement between us.We also assert that the results presented need to be interpreted in the context of race as a social construct.As such, racial disparities in treatment outcomes most likely reflect broader differences in socioeconomic factors and access to care.We initially aimed to assess participants' socioeconomic status and social determinants of health in our analyses.Unfortunately, such variables were not fully captured in the primary studies, and we could not design analyses that addressed this aim.Several pharmaceutical companies have publicly committed to achieving racially and ethnically diverse participation in their global clinical trials [3].This aligns with current guidance from key regulatory authorities [4][5][6].The focus on diversity in clinical trials and better collection of more granular demographic data will allow for future studies on the impact of socioeconomic, cultural, and practice-based differences that underpin discrepancies in treatment outcomes.
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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 | 0.014 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.034 | 0.027 |
| Insufficient payload (model declined to judge) | 0.006 | 0.008 |
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".