Reply to Reiffel—Numbers Don’t Lie—But They Tell Only Part of the Story
Bibliographic record
Abstract
We appreciate Reiffel's 1 interest in our paper, 2 and the opportunity it provides to reiterate important caveats that apply when interpreting research findings.First, Reiffel 1 mentions a concern with possible misclassification of diagnostic codes in administrative datasets.We agree, and we mentioned this among our limitations. 2However, this concern is mitigated by the fact that the data were generated by trained medical personnel and that these datasets have been used in over in 2700 peer-reviewed studies (https:// www.merative.com/real-world-evidence).Second, Reiffel 1 notes, again as we mentioned in the original article, 2 that propensity-score methods can adjust only for measured covariates.However, Reiffel's specific concern about the impact of beta-blockers was dealt with by exclusion, as both exposure groups were selected without any concomitant beta-blockers.Similarly, sodium-glucose cotransporter-2 (SGLT-2) medication use was not unbalanced between the groups, as these were not on the market at the time of the data acquisition.Third, the choice of research question, and as a consequence, the chosen outcome remains an essential element in any study.Reiffel 1 argues that only unplanned atrial fibrillation hospitalizations should have been considered as an outcome, but most readers would (i) find the definition of "unplanned" to be potentially arbitrary and subjective; and (ii) consider that outcome to be of lesser importance than our a priori chosen total repeat cardiovascular outcome measure.Finally, as Reiffel 1 suggests, different research questions, including the effect of drug choice on atrial fibrillation (AF) symptoms or AF burden, could have been chosen, but they quite simply were not our research question.Accordingly, we disagree with Reiffel's 1 assertion that "numbers can lie,"; rather, the issue is not with the numbers per se, but with how they are generated and interpreted.We feel that the generation and interpretation of these numbers support our conclusion that "given the large burden of disease with atrial fibrillation, a pressing need remains to reproduce and expand these research findings in different settings."
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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 teacher head, 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".