Heterogeneous Treatment Response by Race Cannot Be Claimed in the Absence of Evidence
Bibliographic record
Abstract
To the Editor: We read with interest the article by Mehanna et al.1 on the exploration of plasma renin activity (PRA) as a predictive biomarker of blood pressure response to hypertensive therapy in European Americans vs. African Americans with essential uncomplicated hypertension. High PRA > 0.65 ng/ml/hour was reported to be predictive for antihypertensive treatment response to metoprolol in European Americans but not in African Americans. The racial groups were claimed to be distinct in their responses because of a “significant” effect (P = 0.04) in the stratum of European Americans, but not in African Americans (P = 0.8). It is a classic fallacy, however, as described by Gelman and Stern,2 to compare the degree of statistical significance against the null of 2 results. The difference between a “significant” and a “not significant” effect is not itself statistically, let alone clinically, significant. If the authors are interested in showing that there is a racially distinct performance of this predictive biomarker, the real question to ask is whether stratum-specific estimates differ from the common effect estimate, not whether they each differ from the null.3 A heterogeneity test on the 2 estimates using the data from Figure 2 shows little evidence to assert that European Americans and African Americans are different in either category (Table 1).
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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 | 0.097 | 0.340 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.062 | 0.052 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".