The Ethical Obligation to Prevent Maternal Mortality during the COVID-19 Pandemic and Beyond
Bibliographic record
Abstract
of COVID-19 infections; the vast majority were among unvaccinated pregnant patients. 4aternal mortality rates in the USA in 2021 were about 24/100,000 live births, while in the United Kingdom, the number was less than nine, and in Canada, it was less than seven, and rates are much higher for Blacks and Hispanics.Maternal mortality, as defined by the Centers for Disease Control and Prevention (CDC), therefore, places the USA far behind its peer nations. 1 This paper was presented at the symposium ZAGREB-New York Ethical and Perinatal Dialogue (1st International Symposium When does human life begin?Ethics, law, and professionalism in reproductive medicine; and Fetal neurology: from short-to the long-term follow-up -how to proceed?Multi-center results on the clinical use of KANET), held on 8-9 October 2022 in Zagreb, Croatia. IntroductIonMaternal mortality is a major global concern.The maternal mortality rate in the USA has for many years exceeded that of other high-income countries, and data show a widening gap between the USA and its peer nations. 1 Although a notable decline in maternal mortality in the USA occurred during the mid-20th century, this progress stalled during the late 20th century.Furthermore, maternal mortality rates have increased during the early 21st century.Since 1987 the number of reported pregnancy-related deaths in the USA has steadily increased from 7.2 deaths/100,000 live births in 1987 to 16.9 deaths per 100,000 live births in 2016.][4] Maternal deaths in the USA further increased during the COVID-19 pandemic.After leveling off around 2015, maternal mortality rates in the USA further increased in 2020 and 2021 by about 20%, with most of the increase in maternal deaths due to COVID-19-related deaths.In 2021 about one in four maternal deaths were due to causes 1,
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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 | 0.005 | 0.146 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".