Parvovirus: Conservative management of fetal anemia and hydrops
Bibliographic record
Abstract
Following the COVID-19 pandemic, Northwestern Europe has experienced a marked increase in congenital parvovirus infections. This rise is attributed to social distancing measures which disrupted the usual seasonal variation of parvovirus B19. Fetal infection may cause severe anemia, thrombocytopenia, and hydrops fetalis, with significant risk of intrauterine death. Therefore, when acute parvovirus B19 infection is confirmed by maternal serology, serial ultrasound surveillance of the middle cerebral artery peak systolic velocity is recommended. Intrauterine transfusion remains the only established therapeutic option for cases of suspected fetal anemia or hydrops but carries risks of fetal loss and procedural-related complications including fetal hemorrhage and exsanguination. This review critically examines current literature on diagnosis, management, perinatal outcomes, and long-term neurodevelopmental sequelae following congenital parvovirus infection and intrauterine transfusion. Additionally, we report our tertiary fetal medicine center's experience during the 2024 epidemic, highlighting a novel conservative management approach for fetuses with parvovirus-related anemia and hydrops fetalis.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".