The association between pre-gravid and first trimester maternal weight and its implications for clinical research studies
Bibliographic record
Abstract
In clinical research, weight measurement in first trimester is often treated as a surrogate for pre-pregnancy weight. The validity of this critical assumption, however, is uncertain. Thus, we sought to prospectively evaluate the relationship between pre-gravid weight and first trimester weight. In this prospective preconception observational cohort study, 474 newly-married women in Liuyang, China, underwent pre-gravid evaluation at median 17.7 weeks before a singleton pregnancy, during which they had weight measurement in first trimester. The relationship between pre-gravid and first trimester weight was assessed by Bland-Altman analysis, Concordance Correlation Coefficient, and Pearson correlation. Mean pre-gravid weight was 49.8 ± 6.4 kg and mean weight in first trimester was 51.1 ± 7.0 kg. The Concordance Correlation Coefficient between pre-gravid and first trimester weight was 0.76 (95% limits of agreement: 0.72-0.80) and Pearson correlation was r = 0.78 (p < 0.0001), indicative of good concordance and correlation. As the timing of the weight measurement in first trimester increased in weekly increments from < 8 weeks to 14 weeks, the Concordance Correlation Coefficient ranged between 0.69 to 0.76 and the Pearson correlation ranged from 0.71 to 0.78 (all p < 0.0001). In conclusion, the observed concordance between pre-gravid weight and weight measured at any point in the first trimester provides a measure of validation for the widespread practice in clinical research of treating first trimester weight measurement as a surrogate for maternal weight before pregnancy.
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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 | 0.288 | 0.508 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".