Adverse perinatal events and maternal interpregnancy weight change: A population‐based observational study
Bibliographic record
Abstract
OBJECTIVE: Mothers whose newborn experiences adversity may neglect their own health to care for their affected infant or following a perinatal death. Weight gain after pregnancy is one measure of maternal self-care. We measured interpregnancy weight gain among women whose child had an adverse perinatal event. METHODS: This population-based observational study included 192 154 primigravid women with two consecutive singleton births in Ontario, Canada. Outcomes included net weight gain, and adjusted odds ratios (aOR) of moving to a higher body mass index (BMI) category between pregnancies, comparing women whose child did versus did not experience either a perinatal death, prematurity, severe neonatal morbidity, major congenital anomaly, or severe neurologic impairment. RESULTS: Perinatal death was associated with a +3.5 kg (95% confidence interval [CI]: 2.1-4.9) net higher maternal weight gain in the subsequent pregnancy. Relative to term births, preterm birth <32 weeks (+3.2 kg, 95% CI: 1.9-4.6), 32-33 weeks (+1.8 kg, 95% CI: 0.7-2.8) and 34-36 weeks (+0.9 kg, 95% CI: 0.6-1.3) were associated with higher net weight gain. Having an infant with severe neonatal morbidity was associated with a +1.2 kg (95% CI: 0.3-2.1) weight gain. Likewise, the aOR of moving to a higher BMI category was 1.27 (95% CI, 1.14-1.42) following a perinatal death, 1.21 (95% CI: 1.04-1.41) after a preterm birth <32 weeks, and 1.11 (95% CI: 1.02-1.22) with severe neonatal morbidity. CONCLUSION: Greater interpregnancy weight gain, and movement to a higher BMI category, are each more likely in a woman whose first-born was affected by certain major adverse perinatal events.
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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 | 0.003 |
| 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 | 0.000 |
| 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".