Bridging the gap between bench and clinic: the importance of understanding the mechanism of iodinated contrast media hypersensitivity
Bibliographic record
Abstract
Since the advent of CT, iodinated contract media (ICM) has become one of the most regularly administered intravenous medications in clinical settings. Although considered generally safe, ICM is one of the most common causes of adverse drug reactions in clinical practice, accounting for more than 2 million adverse reactions worldwide. Currently, there are few useful tools to diagnose patient hypersensitivity, with the major limitation being the lack of consensus regarding the mechanisms of hypersensitivity to ICM. While there is an overwhelming abundance of literature pertaining to clinical features including incidence, symptomatology, and risk, few studies have further investigated the underlying mechanisms behind their clinical observations. Of the available literature discussing pathophysiology, most primary studies were completed over 20 years ago, since which the molecular characteristics of ICM have changed. Furthermore, many reviews mentioning pathophysiology fail to adequately emphasize the clinical importance of understanding the molecular pathways involved in hypersensitivity. In this review, we aim to emphasize the clinical relevance of pathophysiology as it relates to the prediction and diagnosis of hypersensitivity reactions to ICM. To this end, we will first briefly characterize hypersensitivity reactions to ICM with respect to epidemiology and clinical presentation. We will then present the existing evidence supporting various proposed mechanisms of hypersensitivity, highlighting the gaps that remain in the mechanistic delineation of both immediate and delayed reactions. Finally, we discuss the possibility of in vitro testing as a way to predict and diagnose hypersensitivity reactions, pending a more complete elucidation of mechanisms.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".