Weight change in middle age poor Mexican women in a 10y period
Bibliographic record
Abstract
To explore the weight change over time and its associated factors in women from semi‐rural area in Mexico. Methods Women participating in a randomized controlled micronutrient supplementation trial during pregnancy in 1998 were followed in 2005 and 2008. Information on weight, physical activity (PA) and socio‐demographic information was collected on each contact (n= 632). Furthermore, in a subsample (n=100) we collected blood samples in order to explore the distribution of SNP LEPTINA 3UTR, and SNP PPARG Pro12Ala. General estimating equations (GEE) were estimated to explore the association of weight change over time, and socio‐demographic, characteristics and changes in PA level. Results Women were on average 23.6±5.5y and had a parity of 2.4±1.4 when were first recruited. The combined prevalence of overweight and obesity increased from38.1% in 1997 to 74.2% and 83.6 % for 2005, and 2008 respectively. The mean BMI change was 5.1±3.3 from 1998–2008. After adjusting for potential confounders, BMI change was associated to parity, (beta coefficient 1.5± 0.05, p< 0.01), and socioeconomic level, showing that women at the highest tertile had a greater increase in BMI (p<0.01). Discussion The overweight and obesity in this sample increased almost 46 percentage points in a 9 years period. In women from poor semi‐rural community, having a wealthier economic status and high parity might be potential risk factors for obesity
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".