The bias in current measures of gestational weight gain
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it