Bibliographic record
Abstract
During pregnancy, vaccine hesitancy, defined as a "delay in acceptance or refusal of vaccination, despite availability of vaccination services" (1) is understandable as mothers worry about possible effects on their unborn children.However, such concerns are exacerbated by widespread misinformation, as occurred during the COVID-19 pandemic.A 2024 systematic review has highlighted the extent to which social media platforms were disseminating untruths, e.g., vaccines are generally unsafe for pregnant woman and they increase the risk of infertility, miscarriage, stillbirth, and congenital defects (2).The false claim that COVID-19 vaccines result in female infertility (3) was not surprising given that the claim has repeatedly been made over many years about vaccines against polio and tetanus, and more recently human papilloma virus, especially in low-middle income countries (4).Whatever the reasons for vaccine hesitancy, which include historical racism (5), the effects have been dramatic.In Europe, a 2024 literature review found that acceptance of vaccination against COVID-19 among pregnant women ranged from 21.3% to 87% and 29.5% to 82.7% for one and two vaccine doses, respectively (6).Given that vaccination protects against severe disease (7, 8), it was inevitable that some women would die unnecessarily if not vaccinated.In the UK, for example, COVID-19 was the second most common cause of maternal death in 2020-2022 contributing to the highest maternal mortality rate in 20 years (9).However, does the biomedical community bear any responsibility for the vulnerability of the pregnant population to these distortions of the truth?At the outset of the COVID-19 pandemic the precise risks for pregnant women were uncertain, although it was known that SARS-COV-1 infection in pregnancy is associated with increased maternal mortality (10).Evidence began to emerge of similar risks for SARS-COV-2 infection from small case series and a living systematic review comparing pregnant and non-pregnant women with COVID-19 (11).Then, a cohort study involving 43 hospitals in 18 countries, showed from as early as 2 March 2020, significantly increased severe infections (relative risk (RR), 3.38; 95% CI, 1.63-7.01)and maternal mortality (RR, 22.3; 95% CI, 2.88-172) in pregnant women with COVID-19 compared to unaffected pregnant women (12).Unfortunately, professional organizations and international bodies did not respond appropriately or quickly enough to the emerging evidence (13).In many countries, pregnant women were not included in the groups targeted for vaccination once vaccines became available; nor were they routinely offered vaccination after publication of the study in October 2021 comparing 10,861 vaccinated pregnant women matched to 10,861 unvaccinated pregnant controls that demonstrated the effectiveness of an mRNA vaccine (14).Concerns about the lack of clinical trial data, especially relating to the safety of the
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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.056 | 0.012 |
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".