Gestational diabetes and large for‐gestational age (LGA) infants are common in a multiethnic population of low‐income pregnant women in Montreal.
Bibliographic record
Abstract
Background Pregnancy is a critical period where maternal nutrition and lifestyle choices have major influences on maternal and child health. Low income, prevalent in single parents who tend to be women (19.7%) and recent immigrants (16.4%), increases the risk of adverse pregnancy outcomes. These pregnant women may not have resources to afford food, shelter and other necessities and may experience health inequities that impact pregnancy. Community‐based interventions using the Higgins' method aim to improve pregnancy outcomes in low‐income women through education and provision of food and supplements, although its consequences on pregnancy outcomes are not clear. Objectives The objective of this study is to describe frequencies and temporal shifts of adverse pregnancy outcomes between 2008 and 2013 in women attending the Montreal Diet Dispensary. Methods A retrospective chart review was undertaken to establish the frequency of pregnancies complicated by preeclampsia, gestational diabetes mellitus (GDM), maternal anemia as well as low birth weight (LBW) and large‐for‐gestational age infants. Results Between 2008 and 2013, 5689 pregnancies were reviewed. Pregnancy complications included 1272 (22%) women with maternal anemia, followed by 683 (12%) and 424 (7%) with GDM and LGA infants, respectively but prematurity (n=231; 4%), LBW (175; 3%), high blood pressure (n=155; 2.7%) and preeclampsia (n=6; 0.001%) were below the general population frequency. Conclusion These data suggest that prevention of LBW and prematurity are well supported by the Higgins method and outcomes such as GDM and LGA infants need to be addressed in low‐income pregnancies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".