Efficacy of Omalizumab therapy in a case of severe atopic dermatitis
Bibliographic record
Abstract
Background Atopic dermatitis (AD) is a common skin disease of childhood which may cause debilitating symptoms and greatly impair the quality of life of the patient and his relatives [1]. Treatment of chronic AD usually focuses on topical regimen of emolients and immunosuppressants, although systemic immunosuppressive therapy is sometimes required in more severe cases. Omalizumab is a humanized monoclonal anti- IgE antibody that binds at the high-affinity receptor (FceRI) binding site that has revealed some potential in the treatment of severe and recalcitrant AD [2]. Case Here, we present the case of a 11-years-old girl who has been under treatment with Omalizumab for the past five years. The patient first presented at 2 months of age with a global and severe AD involving. She was severely atopic with total IgE levels of 121,000, mild asthma, and multiple food allergies. Treatment with oral prednisone, cyclosporin, azathioprine and intravenous immunoglobulins did not improve her skin symptoms significantly. She was hospitalised multiple times for skin infections attributed to the disease and immunosuppressive medication. Treatment with Omalizumab was initiated at 6 years of age. Four months later, SCORAD index improved significantly. Since, her follow-up has been almost free of any remarkable event and treatment with Omalizumab has been well tolerated.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".