Multiple births as a mediator than a confounder in <scp>ART</scp> research
Bibliographic record
Abstract
Magnus and colleagues'1 research estimating the risk of stroke following births using assisted reproductive technologies (ART) offers important insights, particularly with its extensive sample size and longitudinal approach. However, we hope for clarification regarding the role of multiple births as a confounder in the authors' analyses. Multiple births may be a result of ART instead of a pre-exposure factor associated with both ART and stroke. Adamson and Baker,2 further supported by Gerris,3 suggest that ART increases the likelihood of multiple births given the practice of transferring multiple embryos to improve pregnancy chances. Thus, multiple births could be considered an outcome of ART—in other words, part of the causal pathway between ART and stroke—rather than a confounder. Furthermore, Dudenhausen and Maier4 and Laine et al.5 found that multiple births increase the risk of complications that could lead to stroke, such as pre-eclampsia, suggesting multiple births mediate the association between ART and stroke rather than confounding the relationship. Adjusting for multiple births, thereby removing part of the effect of ART on stroke risk that may operate through the increased likelihood of multiple births, can underestimate ART's association with stroke. Nicholas J. McCaughey and Amy Dodge conceptualized the manuscript. Nicholas J. McCaughey wrote the manuscript and Amy Dodge contributed feedback and revisions. We want to thank Azar Mehrabadi of Dalhousie University’s Departments of Obstetrics & Gynaecology and Pediatrics for encouraging publication of this article. All authors declare they have no conflicts of interest. Data sharing is not applicable to this article as no datasets were generated or analysed.
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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 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.018 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".