Risk Factors for Preeclampsia in Women from Colombia: A Case-Control Study
Bibliographic record
Abstract
BACKGROUND: Preeclampsia (PE) is a multi-causal disease characterized by the development of hypertension and proteinuria in the second half of pregnancy. Multiple risk factors have been associated with the development of PE. Moreover, it is known that these risk factors vary between populations from developed and developing countries. The aim of this study is to identify which risk factors are associated with the development of preeclampsia (PE) among Colombian women. METHODS: A multi-centre case-control study was conducted between September 2006 and July 2009 in six Colombian cities. Cases included women with PE (n = 201); controls were aged-matched pregnant women (n = 201) without cardiovascular or endocrine diseases for a case-control ratio of 1:1. A complete medical chart, physical examination and biochemical analysis were completed before delivery. Multivariable logistic regression was used to calculate odds ratios (OR) and 95% confidence intervals (CI) of potential risk factors associated with PE. RESULTS: The presence of factors present in the metabolic syndrome cluster such as body mass index >31 Kg/m2 (OR = 2.18; 1.14-4.14 95% CI), high-density lipoprotein <1.24 mmol/L (OR = 2.42; 1.53-3.84 95% CI), triglycerides >3.24 mmol/L (OR = 1.60; 1.04-2.48 95% CI) and glycemia >4.9 mmol/L (OR = 2.66; 1.47-4.81 95%CI) as well as being primigravidae (OR = 1.71; 1.07-2.73 95% CI) were associated with the development of PE, after adjusting for other variables. CONCLUSION: Factors present in the cluster of metabolic syndrome and primigravidity were associated with a greater risk of PE among Colombian women. Understanding the role of this cluster of risk factors in the development of PE is of crucial importance to prevent PE and remains to be determined.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".