Bibliographic record
Abstract
We thank Dr. Wang for his observations (1). Dr. Wang argues that a successful intervention to increase birth weight would have little impact on blood pressures, whereas interventions focused on later-life weight have a much greater potential for population blood pressure reduction (1). However, Dr. Wang may be confusing issues of etiology, effect size, and the public health implications of the relationship between birth weight, current weight, and blood pressure. In our study, we addressed an etiologic question about the effect of birth weight on blood pressure, by showing that the negative association between birth weight and blood pressure upon adjustment for current weight was not easily explained by collider-stratification bias (2). As mentioned by Dr. Wang and in line with other studies (3), our results also indicated that the effect size of birth weight on blood pressure is quite small compared with the effect of current weight. Nevertheless—and this is where we disagree with Dr. Wang—that does not necessarily imply that the public health implications of the association between birth weight and blood pressure are negligible compared with the implications of later-life body weight. Indeed, the public health implication is constrained by the existence of implementable interventions to modify either birth weight or later-life weight (4). On the one hand, several interventions (such as encouraging maternal smoking cessation or preventing insufficient weight gain during pregnancy) could increase offspring birth weight and, eventually, reduce blood pressure. On the other hand, besides gastric bypass surgery, there is no easily implementable and efficient intervention for obtaining long-term, substantial body-weight reduction in later life and, hence, for having any “tangible” effect on blood pressure. Indirectly, Dr. Wang also points to a major issue in the assessment of the effect of birth weight or later-life weight on blood pressure—that is, a potential violation of the causal consistency assumption (4), as different interventions changing birth weight or later-life body weight can have very different effects on blood pressure. Nevertheless, we agree that, if successful, prevention of excess body-weight gain throughout the life course could have a large impact on the prevention of elevated blood pressure (5). Conflict of interest: none declared.
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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 | 0.008 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.102 | 0.095 |
| Insufficient payload (model declined to judge) | 0.009 | 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".