Gestational diabetes: the influence of sociodemographic marginalization on severity of hyperglycemia at diagnosis, glycemic management and perinatal outcomes
Bibliographic record
Abstract
Gestational diabetes mellitus (GDM) is defined as glucose intolerance (hyperglycemia) first recognized during pregnancy, and affects approximately 7% of all pregnancies – over 135 000 cases per year in the United States alone. Estimates suggest that gestational diabetes prevalence has increased by 10-100% during the past 20 years, particularly among certain ethnic groups and individuals with low socioeconomic status (SES), yet only five studies have analyzed the influence of sociodemographic conditions on this disease. There is evidence that age, obesity, parity, ethnicity and family history are risk factors for GDM. Efforts to reduce the risk of GDM have been focused on clinical and behavioural factors and largely ignored the social and spatial patterning of this disease. I conducted a retrospective patient chart review of 538 women treated for GDM at the Toronto East General Hospital, living in 307 geographic areas to assess the role of sociodemographic marginalization in severity of hyperglycemia at diagnosis, glycemic management and adverse perinatal outcomes. Sociodemographic marginalization was found to be associated with more severe GDM diagnostic test values and poor glycemic management after controlling for known risk factors (age, parity, GDM in previous pregnancy and family history). Severity of hyperglycemia at GDM diagnosis was also associated with approximately 35% increased likelihood of poor glycemic management outcomes and poor glycemic control contributed an additional 65%-69% increased risk for a traumatic birth event. Results indicate that severity of hyperglycemia at GDM diagnosis and glycemic management is patterned by sociodemographic disparity, which have practical implications for a focus on pre-, peri- and post-natal care amongst residents of lower socioeconomic geographies in Canada.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".