Management of anaphylaxis in primary care: Canadian expert consensus recommendations
Bibliographic record
Abstract
BACKGROUND: Anaphylaxis is often managed inadequately. We used findings from a systematic review of gaps in anaphylaxis management to develop evidence-based recommendations for gaps rated as clinically important by a panel of Canadian allergy experts. METHODS: The nominal group technique (NGT) consensus methodology was used to develop evidence-based recommendations for the management of anaphylaxis in primary care. Physician-specific gaps from our systematic review were prioritized by consensus meeting participants in two rounds, which involved the rating, discussion, and re-rating of gaps. Using current anaphylaxis guidelines, recommendations were then developed for each category of gaps that were identified by the panel as clinically important. RESULTS: Thirty unique physician gaps from the systematic review were categorized according to gaps of knowledge and anaphylaxis practice behaviors. The panel rated diagnosis of anaphylaxis, and when and how to use epinephrine auto-injectors as clinically important knowledge gaps; and rated infrequent or delayed epinephrine administration, low rate of auto-injector prescription, and infrequent or no referrals to allergy specialists after a reaction as important practice behavior gaps. Evidence from four guidelines was used to support the consensus recommendation statements for three resulting categories of gap themes: anaphylaxis management, epinephrine use, and follow-up care. CONCLUSION: We used an NGT consensus methodology to develop an educational resource for primary care physicians and allergists to better understand how to manage patients with anaphylaxis. Next steps include testing our findings against observed data in primary care settings and to develop other strategies or tools to overcome gaps in anaphylaxis management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.111 | 0.244 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.023 | 0.014 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.011 | 0.007 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".