Platelet‐activating factor acetylhydrolase is a biomarker of severe anaphylaxis in children
Bibliographic record
Abstract
BACKGROUND: There is limited ability to predict the severity of allergic reactions in children. Data derived predominantly from adults have implicated the platelet-activating factor pathway as a potential contributor to severe anaphylaxis. In this study, we sought to prospectively assess involvement of key components of the platelet-activating factor pathway in pediatric patients with anaphylaxis. METHODS: Forty-six pediatric patients (<18 years) presenting with acute anaphylaxis were assessed. Anaphylaxis severity was graded and serum anaphylaxis markers were measured acutely and in 36 children who returned for follow-up >4 weeks after their acute presentation. These markers were compared with pediatric laboratory reference sera. RESULTS: Severe anaphylaxis was experienced by 12/46 (26%) and mild-moderate anaphylaxis in 34/46 (74%) children. Platelet-activating factor acetylhydrolase (PAF-AH) activity was inversely associated with severe anaphylaxis: 9/12 children with severe anaphylaxis had reduced PAF-AH activity as compared with 14/34 with mild-moderate anaphylaxis (p < .05). Furthermore, 3/3 children who required intensive care had markedly reduced mean PAF-AH (nmol/ml/min) (13.73, 95%CI: 7.42-20.03) versus 20/23 who required ward/emergency department care (17.81, 95%CI: 16.80-18.83; p < .05). In children with anaphylaxis, PAF-AH during acute anaphylaxis was unchanged relative to the child's basal levels (mean, 17.26, 95%CI: 16.10-18.42 vs 17.50, 95%CI: 16.21-18.78, p = .63) and was lower than healthy pediatric controls (mean 19.21; 95%CI:18.21-20.21; p < .05). CONCLUSION: Decreased serum PAF-AH activity is a biomarker of severe anaphylaxis. Levels of this enzyme do not change from basal levels during acute anaphylaxis. Our results show that PAF-AH is a biomarker of anaphylaxis severity in children. This key regulatory enzyme may modulate susceptibility to severe anaphylaxis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".