The bias in current measures of gestational weight gain
Bibliographic record
Abstract
Conventional measures of gestational weight gain (GWG), such as average rate of weight gain, are likely to be correlated with gestational duration. Such a correlation could introduce bias to epidemiological studies of GWG and adverse perinatal outcomes because many perinatal outcomes are also correlated with gestational duration. This study aimed to quantify the extent to which currently used GWG measures may bias the apparent relationship between maternal weight gain and risk of preterm birth. For each woman in a provincial perinatal database registry (British Columbia, Canada, 2000-2009), a total GWG was simulated such that it was uncorrelated with risk of preterm birth. The simulation was based on serial antenatal GWG measurements from a sample of term pregnancies. Simulated GWGs were classified using three approaches: total weight gain (kg), average rate of weight gain (kg/week) or adequacy of GWG in relation to Institute of Medicine recommendations. Their association with preterm birth ≤32 weeks was explored using logistic regression. All measures of GWG induced an apparent association between GWG and preterm birth ≤32 weeks even when, by design, none existed. Odds ratios in the lowest fifths of each GWG measure compared with the middle fifths ranged from 4.4 [95% confidence interval (CI) 3.6, 5.4] (total weight gain) to 1.6 [95% CI 1.3, 2.0] (Institute of Medicine adequacy ratio). Conventional measures of GWG introduce serious bias to the study of maternal weight gain and preterm birth. A new measure of GWG that is uncorrelated with gestational duration is needed.
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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 | 0.086 | 0.245 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".