Double‐blind, placebo‐controlled, dose‐ranging study of new recombinant hypoallergenic Bet v 1 in an environmental exposure chamber
Bibliographic record
Abstract
BACKGROUND: Recombinant allergens offer a tool for improving specific immunotherapy (SIT). OBJECTIVE: To find the optimal dose of a new hypoallergenic folding variant of recombinant Bet v 1 (rBet v 1-FV) as SIT for patients with birch pollen allergy. METHODS: Before SIT, thirty-seven adult patients were exposed for eight hours in an environmental exposure chamber (EEC) to birch pollen at an average concentration of 3500 ± 500 grains/m(3) , then randomized to four maintenance dose groups of rBet v 1-FV and one placebo group: 20 μg (n = 7), 80 μg (n = 8), 160 μg (n = 7), 320 μg (n = 8), and placebo (n = 7). Patients were treated for 10 weeks with weekly injections and then re-exposed in the EEC. The optimal dose for SIT was assessed using efficacy results from the EEC, IgG responses, and tolerability. RESULTS: Thirty-six patients were evaluable for efficacy assessment. The total symptom score significantly decreased in all active groups compared with placebo (-18.8% for placebo patients; -71.9%, P = 0.0022 for 20 μg; -75.6%, P = 0.0007 for 80 μg; -81.8%, P = 0.0009 for 160 μg; -78.3%, P = 0.0003 for 320 μg). IgG1 increased significantly in all active groups compared to placebo. All four active doses were well tolerated, no serious adverse event occurred; two Grade II reactions, according to EAACI classification, were observed, one in each of the 160- and 320-μg groups. CONCLUSIONS: Considering efficacy, immunological response, and tolerability, a maintenance dose of 80 μg of rBet v 1-FV appears to be the ideal dose for allergen immunotherapy in birch pollen allergic patients.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".