Considerations in the management of hereditary angioedema due to C1-INH deficiency in women of childbearing age
Bibliographic record
Abstract
Hereditary angioedema (HAE) is a rare, autosomal disorder that manifests with unpredictable episodes of severe swelling of the skin and mucous membranes. These attacks can be highly disfiguring and range in severity from mild to-in cases of airway swelling-life-threatening. Fluctuations in female sex hormones-such as the changes that occur during puberty, menses, contraceptive use, pregnancy, and menopause-can all affect the frequency and severity of HAE attacks. Disease management decisions for women of childbearing age may be more complex and require additional considerations since they could develop complications related to contraception, pregnancy, labor, delivery, and lactation. In addition, some HAE treatment options are contraindicated during pregnancy. Discussions about medications used to treat HAE should include a risk-benefit assessment of the woman's health status, her preferences, and other factors that are relevant to the choice of therapy. Planning prophylactic therapies that are effective and safe before, during, and after pregnancy can prevent gaps in treatment, ensure continuity of care, and reduce both disease burden and risk of adverse fetal outcomes. The 2020 US Hereditary Angioedema Association (HAEA) Medical Advisory Board and 2021 World Allergy Organization/European Academy of Allergy and Immunology (WAO/EAACI) Guidelines outline key considerations for managing HAE in females of childbearing age (15-45 years), with the goal of improving treatment efficacy and safety for this cohort of patients. Treatment decisions made in a collaborative manner involving the patient, HAE specialist and obstetric/gynecologic specialist, is the best approach to ensure optimal HAE management and safety in this patient population.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".