Biphasic anaphylaxis in a Canadian tertiary care centre: an evaluation of incidence and risk factors from electronic health records and telephone interviews
Bibliographic record
Abstract
BACKGROUND: Our previous 2007 study reported a 19.4% rate of biphasic anaphylaxis in Kingston, Ontario. Since then, few updates have been published regarding the etiology and risk factors of biphasic anaphylaxis. This study aimed to describe the incidence of and predictors of biphasic anaphylaxis in a single centre through a retrospective evaluation of patients with diagnosed anaphylaxis. METHODS: From November 2015 to August 2017, all patients who presented to the emergency department at two hospital sites in Kingston given a diagnosis of "allergic reaction," "anaphylaxis," "drug allergy," or "insect sting allergy," were evaluated. Patients were contacted sometime after ED discharge to obtain consent and confirm symptoms and timing of the reaction. A trained allergist determined if criteria for anaphylaxis were met and categorized the reactions as being uniphasic, biphasic, or non-anaphylactic biphasic. A full medical review of the event ensued, and each type of anaphylactic event was statistically compared. RESULTS: Of 138 anaphylactic events identified, 15.94% were biphasic reactions, 79.0% were uniphasic, and 5.07% were classified alternatively as a non-anaphylactic biphasic reaction. The average time of a second reaction was 19.0 h in patients experiencing biphasic reactivity. For biphasic anaphylaxis, the symptom profiles of second reactions were significantly less severe (p = 0.0002) compared with the initial reaction but significantly more severe than non-anaphylactic biphasic events (p < 0.0001).No differences of management were identified between events. CONCLUSION: The incidence of biphasic reactions in this cohort was 15.94% and the average second-phase onset was 19.0 h. In biphasic reactivity, it appears that the symptom profile second reaction is less severe compared to the first reaction.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".