Incidence and risk factors of gestational diabetes mellitus in Goba town, Southeast Ethiopia: a prospective cohort study
Bibliographic record
Abstract
OBJECTIVE: Gestational diabetes mellitus (GDM) is becoming a public health concern in low/middle-income countries, and is known to cause severe morbidity and mortality for mothers and newborns. However, evidence reported for the incidence and risk factors of GDM is scant in Ethiopia. We aimed to assess the incidence of, and risk factors for, GDM in Goba town, Southeast Ethiopia. DESIGN: Prospective cohort study. SETTING: Goba town, Southeast Ethiopia. PARTICIPANTS: Four hundred eighty pregnant women on antenatal care follow-up from 30 April to 30 September 2021. PRIMARY AND SECONDARY OUTCOMES: Incidence and risk factors of GDM using fasting capillary blood glucose. Log-binomial model was used to identify the risk factors of GDM. Adjusted relative risk (aRR), along with 95% CIs, were calculated to estimate the strength of associations. RESULTS: The cumulative incidence rate of GDM in this study was 15.7% (95% CI: 12.3% to 19.2%). Being unemployed (aRR=2.73; 95% CI: 1.36 to 5.47), having a family history of diabetes mellitus (DM) (3.01; 2.09 to 4.35), low physical activity (2.43; 1.11 to 5.32), inadequate dietary diversity (1.48; 1.29 to 1.92), anaemia (2.51; 1.32 to 3.54) and antenatal depression (4.95; 3.35 to 7.31) were significantly associated with GDM. CONCLUSION: The cumulative incidence of GDM was relatively high among the study participants. Having antenatal depression symptoms, low physical activity, inadequate dietary diversity, being unemployed, anaemia and a family history of DM were significant risk factors for GDM.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".