Efficacy of high-dose inhaled salbutamol for the treatment of gastro-intestinal anaphylaxis.
Bibliographic record
Abstract
Background: Abdominal pain is a frequent symptom of IgE-mediated food allergy with limited therapeutic options. Visceral smooth muscle cell relaxation can be induced by activation of the beta-adrenergic receptors. Objective: To evaluate the efficacy of inhaled salbutamol empirically used to relieve abdominal pain caused by IgE-mediated allergic reactions at one center. Methods: All double-blind placebo-controlled food challenges to peanut performed at CHU Sainte-Justine between 2016 and 2020 were reviewed to identify patients that presented abdominal pain as part of their reaction. The primary outcome measure was the delay between the initiation of therapy and improvement of abdominal pain. It was compared between patients that had received inhaled salbutamol as part of their treatment and those that did not. Linear regression was performed to control for potential confounders. Results: During the study period, 174 positive DBPCFCs were performed, including 116 for peanut allergy. Of these, 77 presented abdominal pain and 49 met the criteria for inclusion in the study. Patients that received salbutamol improved significantly faster (median 14 minutes; range 5-66) than those that did not (61 minutes range 5-194) (p<0.0001). In the linear regression, only the administration of salbutamol and emesis were found to independently accelerate the recovery of abdominal pain, each reducing the time to improvement by an average of 61 ±10 minutes (p<0.0005) and 44 ±13 minutes (p<0.0005), respectively. Conclusion: This retrospective study provides low-quality evidence of a large effect for salbutamol in the treatment of gastro-intestinal anaphylaxis. Further investigation in randomized controlled trials would be warranted.
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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 | 0.001 |
| 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) | 0.001 | 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".