Comparison of international guidelines for the emergency medical management of anaphylaxis
Bibliographic record
Abstract
BACKGROUND: Guideline-based treatment approaches for managing anaphylaxis are widely believed to result in good outcomes, but the strength of evidence underpinning the recommendations made therein is unclear. OBJECTIVE: To identify and compare national guidelines for the emergency medical management of anaphylaxis and to describe the extent to which the evidence base in support of key recommendations is made clear. METHODS: We systematically searched key medical databases and contacted the World Allergy Organization and anaphylaxis charities in several countries to identify national guidelines. Full text copies of relevant papers were obtained and, where necessary, translated. Data were abstracted onto a customized data extraction sheet; this process was independently checked by a second reviewer. RESULTS: Guidelines originating from Australia, Canada, Russia, UK, Ukraine and the USA were identified. While these were in agreement on the broad principles of management, there were important variations in relation to the treatments to be used and the dose and route of administration of these preparations. Most guidelines failed to make clear the strength of evidence underpinning the recommendations being made. CONCLUSIONS: There are important international differences in the recommended emergency management of anaphylaxis. It is important that an agreed core evidence-based guideline for the management of anaphylaxis is now developed, which can then be adapted for national/local use. Clinicians need to be aware of the limitations of existing guidelines.
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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 | 0.045 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".