Canadian expert consensus: management of hypersensitivity reactions to intravenous iron in adults
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Rare but potentially life-threatening hypersensitivity reactions can occur during the administration of intravenous iron. To provide guidance to healthcare professionals caring for adults receiving intravenous iron, a panel of 10 Canadian clinical experts developed a practical algorithm for the identification and management of hypersensitivity reactions to intravenous iron. MATERIALS AND METHODS: A systematic search of PubMed to February 2018 was performed. Articles related to hypersensitivity reactions were selected for review. The algorithm was developed during a 1-day live meeting based on the literature review and clinical expertise where evidence was lacking. The algorithm was then refined through an iterative process involving a web-based platform and virtual meetings. RESULTS: The algorithm provides guidance to healthcare professionals in preparing for and administering IV iron, as well as recognizing and managing hypersensitivity reactions to intravenous iron. Considerations for re-challenging patients who have experienced prior reactions are provided. CONCLUSION: Healthcare professionals who are involved in the care of patients receiving intravenous iron should be trained to anticipate, recognize and manage hypersensitivity reactions to intravenous iron to optimize patient care.
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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".