Systemic reactions to subcutaneous allergen immunotherapy: real-world cause and effect modelling
Bibliographic record
Abstract
BACKGROUND: Subcutaneous immunotherapy (SCIT) is an effective treatment for allergic rhinoconjunctivitis. However, adverse events, including life-threatening systemic reactions, may occur. The purpose of this project is to identify risk factors for systemic reactions to SCIT and to provide practice-based solutions using a quality improvement (QI) framework. METHODS: A QI initiative was performed in a hospital-based, Canadian Allergy clinic administering SCIT in a 12-month period. RESULTS: A total of 4242 injections of SCIT were performed over a period of 12 months. Of these, 10 injections resulted in a systemic reaction requiring epinephrine administration (i.e., an incidence of 1 in 424 injections, or 0.24%). Eight patients had at least one documented risk factor for a systemic reaction, and six had multiple risk factors. Major risk factors included seasonal exacerbation of allergic rhinitis, uncontrolled asthma, and an error in route of administration. All reactions occurred with the highest allergen extract concentration. CONCLUSION: This QI initiative highlights the need for improved patient and health care practitioner education and pre-administration screening. We suggest several considerations for SCIT administration: provide patients with written information on safety; screen patients before injections, including a review of treatment plan adherence and asthma control; adjust dosing to slow down buildup of the most concentrated immunotherapy extract, particularly in high risk patients; and apply additional safety measures in patients with multiple risk factors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".