Role of omalizumab in insulin hypersensitivity: a case report and review of the literature
Bibliographic record
Abstract
BACKGROUND: Insulin allergy is a rare yet severe side effect of exogenous insulin use. Management typically involves use of alternative antihyperglycaemic agents, symptom control with antihistamines, use of different insulin formulations, and induction of tolerance with incremental doses of insulin. This treatment regimen is not always successful, and the use of omalizumab, an anti-IgE monoclonal antibody, has been used to induce tolerance to insulin. CASE REPORT: G.M. is a 62-year-old man with Type 2 diabetes mellitus. His condition was not optimized on oral agents, and insulin therapy was required. G.M. had anaphylaxis to insulin NPH, and subsequent skin-prick testing was positive to insulin aspart, insulin NPH, insulin glulisine, insulin detemir, regular insulin, insulin glargine 100 units/ml and insulin glargine 300 units/ml. He received incremental doses of several insulin formulations; however, he experienced diffuse urticaria preventing optimal glycaemic control. Three successful cases have been described in the literature of omalizumab inducing tolerance to exogenous insulin; therefore, G.M. was started on omalizumab. He subsequently tolerated treatment doses of insulin glulisine and insulin detemir with no allergic reactions and with improvement in glycaemic control. CONCLUSION: To our knowledge, this is the first described case of allergy to insulin glargine 300 units/ml and reiterates the potential use of omalizumab in insulin allergy. Further research is warranted to determine if omalizumab should be considered standard of care in difficult-to-treat insulin hypersensitivity.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".