Clinical Outcomes of COVID-19 Infection in Pregnant and Nonpregnant Women: Results from The Philippine CORONA Study
Bibliographic record
Abstract
OBJECTIVE: Our study determined the association of pregnancy with various clinical outcomes among women with COVID-19 infection. METHODS: We conducted a retrospective, cohort, subgroup analysis of the Philippine CORONA Study datasets comparing the clinical/neurological manifestations and outcomes of pregnant and nonpregnant women admitted in 37 Philippine hospitals for COVID-19 infection. RESULTS: We included 2448 women in the analyses (322 pregnant and 2.126 nonpregnant). Logistic regression models showed that crude odds ratio (OR) for mortality (OR 0.26 [95% CI 0.11, 0.66]), respiratory failure [OR 0.37 [95% CI 0.17, 0.80]), need for intensive care (OR 0.39 [95% CI 0.19, 0.80]), and prolonged length of hospital stay (OR 1.73 [95% CI 1.36, 2.19]) among pregnant women were significant. After adjusting for age, disease severity, and new-onset neurological symptoms, only the length of hospital stay remained significant (adjusted OR 1.99 [95% CI 1.56,2.54]). Cox regression models revealed that the unadjusted hazard ratio (HR) for mortality (HR 0.22 [95% CI 0.09, 0.55]) among pregnant women was statistically significant; however, after adjustment, the HR for mortality became nonsignificant. CONCLUSION: We did not find a significantly increased risk of mortality, respiratory failure, and need for ICU admission in pregnant women compared with nonpregnant women with COVID-19. However, the likelihood of hospital confinement beyond 14 days was twice more likely among pregnant women than nonpregnant women with COVID-19.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".