Maternal known drug allergy and long‐term dermatological morbidity of the offspring
Bibliographic record
Abstract
Drug allergy is associated with adverse short-term perinatal outcomes such as caesarian delivery and preterm delivery. The aim of the present study was to determine whether being born to a mother with known drug allergy increases the risk for long-term dermatological morbidity of the offspring. A population-based cohort study, comparing long-term dermatological morbidity of offspring to mothers with and without known drug allergy, was conducted. Dermatological morbidity was assessed up to the age of 18 years according to a predefined set of ICD-9 codes associated with hospitalization of the offspring. A Kaplan-Meier survival curve was used to compare cumulative incidence of long-term dermatological morbidity, and a Cox proportional hazards model was constructed to control of confounders. During the study period, 243,682 deliveries met the inclusion criteria, of them 4% (n = 9756) were of mothers with known drug allergy. Offspring born to mothers with known drug allergy had higher rates of long-term dermatological morbidity Likewise, the cumulative incidence of long-term dermatological morbidity was higher as compared with those without known drug allergy (Kaplan-Meier log-rank P = .021). Using a Cox proportional hazards model, controlling for confounders, being born to a mother with known drug allergy was found to be an independent risk factor for long-term dermatological morbidity of the offspring (adjusted HR 1.2, 95% CI 1.03-1.33, P = .016). Being born to a mother with known drug allergy is independently associated with higher risk for long-term dermatological morbidity of the offspring.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".