Response to "Calcium Channel Blocker Therapy in Black Hypertensive Patients"
Bibliographic record
Abstract
To the Editor: We thank Brewster and van Montfrans for their interest in our recent article.1 We meta-analyzed US studies of black and white hypertensive patients in order to assess evidence for differential treatment response to calcium-channel blockers (CCBs).2 Our results suggest that there is no rational basis for privileging racial identity of patients as a basis for treatment decisions regarding CCB monotherapy. We included only studies that recruited black and white patients with a uniform set of inclusion and exclusion characteristics in order to protect internal validity, since studies involving only one racial group may be idiosyncratic with respect to many factors, and therefore would threaten a valid black–white contrast. Brewster and van Montfrans assert that “excluding trials in black people only might create biased review results,” but this concern is rooted in the mistaken notion that the parameter of interest must be the black treatment effect. When the parameter of interest is the treatment effect disparity, inclusion of trials with only one or the other group is clearly the greater threat to validity.
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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 | 0.019 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".