Invited Commentary: Aligning Methodological Research on Pregnancy Weight Gain With the Questions That Matter Most for Public Health Guidelines
Bibliographic record
Abstract
The inherent correlation between the total amount of weight gained in pregnancy and the duration of pregnancy creates major methodological challenges in the study of pregnancy weight gain. In this issue (Am J Epidemiol. 2022;191(10):1687-1699), Richards et al. examine the extent to which different measures of pregnancy weight gain (including covariate adjustment for gestational age and standardizing weight gain for gestational duration using a pregnancy weight gain chart) are able to disentangle the effects of low weight gain on perinatal health from the role of younger gestational age at delivery for 3 outcomes: small-for-gestational-age birth, cesarean delivery, and low birth weight. While methodological research to understand how to best disentangle the effects of gestational weight gain from pregnancy duration is valuable, we argue that the practical utility of this type of research would be increased by aligning the specific research questions more closely with health outcomes on which evidence is most needed-those not considered in current weight gain guidelines due to lack of high-quality evidence (such as pre-eclampsia and stillbirth). Further, evaluations of weight gain charts should separate out the potential for bias introduced by the use of a normative chart per se from the use of a chart unsuitable for the study population.
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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 | 0.028 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.075 | 0.069 |
| Insufficient payload (model declined to judge) | 0.008 | 0.010 |
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".