Serum free IgE guided dose reduction of omalizumab: a case report
Bibliographic record
Abstract
BACKGROUND: Omalizumab is a human IgG1 antibody against IgE used as a therapy for sever asthmatic patients with asthma. According to the guidelines of the Global Initiative for Asthma, omalizumab is an add-on drug at treatment step 5 that is used for severe asthma patients who are allergic to perennial allergens. The effects of omalizumab for severe asthma therapy have been validated in multiple clinical studies. However, the long-term effects of omalizumab on IgE production and possibility of resetting of administration dose of omalizumab remain unknown. CASE PRESENTATION: The serum total and free IgE levels were measured over time in a 63-year-old female patient with allergic asthma who was administered 375 mg omalizumab biweekly for 36 months. Her symptoms did not worsen and clinical course remained favorable after reducing the dose to 375 mg per month. The serum free IgE levels temporarily increased following a dose reduction of omalizumab. The serum free IgE trough level temporarily increased at 4 weeks after capable to reduce the dosage; however, thereafter, the serum free IgE level decreased to desired levels (below 30 ng/mL). CONCLUSIONS: The present case shows the possibility of reducing the dose following the long-term use of omalizumab. Considering the high medical cost of omalizumab, the dose reduction may be a viable option. It may be useful to measure the serum free IgE level to appropriately identify patients in whom the dose can be reduced, and to carefully monitor the clinical course.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".