Biphasic Anaphylaxis: A Review of the Incidence, Characteristics and Predictors
Bibliographic record
Abstract
While it has been recognized for over a quarter century that anaphylactic reactions have the potential to follow a biphasic course, reports on the incidence of biphasic anaphylaxis are conflicting, and the search for reliable predictive factors of such responses has been challenging. Further adding to the complexity of this clinical entity are the widely variable durations of the asymptomatic window, and the similarly variable reports on second phase severity. This review aims to provide the health care professional with a better understanding of the true incidence, nature, and risk factors for this type of reactivity by consolidating and summarizing the available literature on the topic of biphasic anaphylaxis. As our body of evidence builds, patterns are emerging to suggest that those patients with an initial presentation requiring more than one dose of epinephrine, those who have life-threatening initial presenting features, and those who otherwise take longer to stabilize, are in this higher risk group, and would be more likely to benefit from prolonged in hospital observation. Conversely, patients who respond rapidly to the immediate administration of epinephrine may be at lower risk, but this finding requires confirmation by others. Further prospective evaluations of biphasic anaphylaxis will greatly aid our understanding of this condition.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".