Clinical evolution of COVID-19 during pregnancy at different altitudes: a population-based study
Bibliographic record
Abstract
Abstract Background The impact of influenza and various types of coronaviruses (SARS-CoV and MERS-CoV) on pregnancy has been reported. However, the current pandemic caused by SARS-CoV-2 continues to reveal important data for understanding its behavior in pregnant women. Methods We analyzed the records of 326,586 non-pregnant women of reproductive age and 7,444 pregnant women with no other risk factor who also had a SARS-CoV-2 RT-PCR result to estimate adjusted prevalence (aP) and adjusted prevalence ratios (aPR) of COVID-19 and its requirement of hospitalization, intubation, ICU admission and case-fatality rates. Adjustment was done through Poisson regressions for age and altitude of residence and birth. Generalized binomial models were used to generate probability plots to display how each outcome varied across ages and altitudes. Results Pregnancy was independently associated with a 15% higher probability of COVID-19 (aPR: 1.15), a 116% higher probability of its following admission (aPR: 2.169) and a 127% higher probability of ICU admission (aPR: 2.275). Also, pregnancy was associated with 84.2% higher probability of developing pneumonia (aPR: 1.842) and a 163% higher probability of its following admission (aPR: 2.639). There were no significant differences in COVID-19 case-fatality rates between pregnant and non pregnant women (1.178, 95% CI: 0.68-1.67). Conclusion Pregnancy was associated with a higher probability of COVID-19, developing of pneumonia, hospitalization, and ICU admission. Our results also suggest that the risk of COVID-19 and its related outcomes, except for intubation, decrease with altitude.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".