Obstetrical practices but not gestational metabolic abnormalities are associated with delayed onset of lactogenesis
Bibliographic record
Abstract
Women with pre‐existing diabetes and obesity experience delayed onset of lactogenesis II (DOLac), but the impact of metabolic abnormalities developed during pregnancy on DOLac is unclear. We aimed to investigate the associations of metabolic status assessed during pregnancy with DOLac (onset of lactogenesis II ≥72 h postpartum). Participants (n=177) underwent a 3‐h oral glucose tolerance test at 30 (25 th , 75 th %tile: 29, 32) weeks gestation, and follow‐up interviews were conducted at 1, 3, and 7 d postpartum. Of 177, 98 (55.4%) women experienced DOLac. Gestational metabolic abnormalities, including gestational diabetes, insulin resistance (HOMA‐IR) and lower insulin sensitivity (Matsuda index), were not associated with DOLac. However, risks for DOLac were increased among women who had an unscheduled C‐section (OR 2.24 [95% CI 1.02, 4.92] vs. spontaneous delivery) and delayed initiation of breastfeeding (2.30 [1.09, 4.86] comparing ≤1 vs. >;2 h) with adjustment for age and ethnicity. Further, women who had an unscheduled C‐section put their infant to the breast at 2.0 (25 th , 75 th %tile: 1.0, 5.1) h, while women who had a spontaneous delivery did so at 1.0 (0.8, 2.0) h. In conclusion, gestational metabolic abnormalities are not associated with DOLac, but obstetrical practices including unscheduled C‐section and delayed timing of breastfeeding initiation increase risk for DOLac. Funding: CDA, CFDR, CIHR
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".