Treating allergic diseases in pregnancy
Bibliographic record
Abstract
Allergic diseases like asthma, allergic rhinitis, or food allergy have a high prevalence in women of childbearing age and may affect up to 30% of this age group. A multitude of immunological changes characterizes pregnancy to create the optimal milieu for the unborn child. Both these immunological changes and pre-conceptional, sub-optimal disease control may affect the severity of the respective allergic disease manifestations during pregnancy and pose a risk for mother and child. Due to apparent limitations in conducting clinical trials, safety data on anti-allergic drugs during pregnancy are limited. This lack of clinical evidence demands to counsel between potential and known risks and benefits of anti-allergic drugs. This includes the potential of disease aggravation in the absence of treatment. By doing so, informed decisions and shared decision-making is facilitated. In particular, in patients with severe asthma, education about the risk of uncontrolled asthma for mother and child should be part of regular care. This review focuses on the management of allergic diseases during pregnancy, maternal counseling, and available information/evidence regarding allergic diseases’ management and treatment during pregnancy. Furthermore, we discuss the challenges of treating patients with allergic diseases and covid-19 during pregnancy.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".