Trends in adverse pregnancy outcomes in Louisiana, 2017 to 2022
Bibliographic record
Abstract
Natural disasters can lead to more adverse pregnancy outcomes (APO). It is unclear if the extended COVID-19 pandemic has impacted APOs and pre-existing conditions among perinatal populations with increased risk of severe maternal morbidity and mortality.A retrospective chart review was conducted of hospital records and birth certificates in the largest birth hospital in Louisiana from 2017 to 2022. Amongst 27,877 births (50.9% White, 38.3% Black, 28.9 ± 5.6 years), gestational diabetes (GDM) was lowest in pre-pandemic conceptions (11.0%, June 2017-May 2019) and rose to 16.4% early pandemic (October 2019-February 2020) but leveled off at 12.2% in peak (March 2020-February 2021) and late pandemic (March 2021-September 2021). Individuals who conceived in early and peak pandemic were 47% (95% CI 33, 63) and 11% (95% CI 2, 20) more likely to develop GDM respectively, compared to pre-pandemic conceptions. Individuals who delivered during early (aRR: 1.54, 95% CI 1.33-1.78), peak (aRR: 1.48, 95% CI 1.32-1.65), and late (aRR: 1.62, 95% CI 1.41, 1.85) pandemic were more likely to develop preeclampsia and HELLP syndrome compared to pre-pandemic conceptions. Individuals were also 17% (95% CI 5, 32) more likely to enter pregnancy with chronic hypertension in peak pandemic compared to pre-pandemic. In paired analysis (n = 3390), individuals with a pandemic conception that occurred early pandemic had a higher risk of developing GDM compared to their pre-pandemic pregnancy (aOR 3.26, 95% CI 1.52, 6.97). Supporting birthing individuals amongst significant stressful events, especially in early gestation, is critical for preventing APOs and severe maternal morbidity and mortality.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".