Maternal and neonatal outcomes associated with gestational diabetes in women from culturally and linguistically diverse backgrounds in Western Australia
Bibliographic record
Abstract
AIMS: To compare maternal and neonatal outcomes for Australian-born women with gestational diabetes mellitus with those of culturally and linguistically diverse and non-culturally and linguistically diverse foreign-born women with gestational diabetes. METHODS: A total of 205,616 singleton births in Western Australia between 1998 and 2006 were examined using multivariate logistic regression. Risks of ten maternal and neonatal outcomes associated with gestational diabetes were compared for pregnancies with gestational diabetes to foreign-born women from both culturally and linguistically diverse and non-culturally and linguistically diverse backgrounds vs. Australian-born women. The same outcomes were also compared for pregnancies without gestational diabetes. RESULTS: Foreign-born culturally and linguistically diverse women were more likely to undergo emergency Caesarean section, but less likely to have pre-eclampsia, an elective Caesarean section or induced labour than Australian-born women. Their infants were less likely to be large for gestational age, require resuscitation or be transferred to specialist care. These differences were also evident among pregnancies without gestational diabetes to culturally and linguistically diverse women, but did not exist between foreign-born non-culturally and linguistically diverse women and Australian-born women with gestational diabetes. CONCLUSIONS: While gestational diabetes places women and infants at increased risk of adverse perinatal outcomes, these outcomes differed for foreign-born women from culturally and linguistically diverse backgrounds when compared with Australian-born women. Further investigation is required to elucidate why being foreign-born and culturally and linguistically diverse reduces the risk of several of these outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".