Estimating bias in derived body mass index in the Maternity Experiences Survey
Bibliographic record
Abstract
INTRODUCTION: The objective of this study was to assess bias in the body mass index (BMI) measure in the Canadian Maternity Experiences Survey (MES) and possible implications of bias on the relationship between BMI and selected pregnancy outcomes. METHODS: We assessed BMI classification based on self-reported versus measured values. We used a random sample of 6175 women from the MES, which derived BMI from self-reported height and weight, and a random sample of 259 women who had previously given birth from the Canadian Health Measures Survey (CHMS), which derived BMI from self-reported and measured height and weight. Two correction equations were applied to self-reported based BMI, and the impact of these corrections on associations between BMI and caesarean section, small-for-gestational age (SGA) and large-for-gestational age (LGA) births was studied. RESULTS: Overall, 86.9% of the CHMS subsample was classified into the same BMI category based on self-reported versus measured data. However, misclassification had a substantial effect on the proportion of women in underweight and obese BMI categories. For example, 14.5% versus 20.8% of women were classified as obese based on self-reported data versus measured data. Corrections improved estimates of obesity prevalence, but over- and underestimated other BMI categories. Corrections had nonsignificant effects on the associations between BMI and SGA, LGA, and caesarean section. CONCLUSION: While there was high concordance in BMI classification based on selfreported versus measured height and weight, bias in self-reported based measures may slightly over- or underestimate the risks associated with a particular BMI class. However, the general trend in associations is unaffected.
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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 | 0.091 | 0.249 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".