Observational long-term follow-up study of rapid food oral immunotherapy with omalizumab
Bibliographic record
Abstract
BACKGROUND: A number of clinical studies focused on treating a single food allergy through oral immunotherapy (OIT) with adjunctive omalizumab treatment have been published. We previously demonstrated safety and tolerability of a rapid OIT protocol using omalizumab in a phase 1 study to achieve desensitization to multiple (up to 5) food allergens in parallel, rapidly (7-36 weeks; median = 18 weeks). In the current long-term, observational study, we followed 34 food allergic participants for over 5 years, who had originally undergone the phase 1 rapid OIT protocol. METHODS: After reaching the maintenance dose of 2 g protein for each of their respective food allergens as a part of the phase 1 study, the long-term maintenance dose was reduced for some participants based on a pragmatic team-based decision. Participants were followed up to 62 months through standard oral food challenges (OFCs), skin prick tests, and blood tests. RESULTS: Each participant passed the 2 g OFC to each of their offending food allergens (up to 5 food allergens in total) at the end of the long-term follow-up (LTFU) study. CONCLUSION: Registry: Clinicaltrials.gov. Registration numbers: NCT01510626 (original study), NCT03234764 (LTFU study). Date of registration: November 29, 2011 (original study); July 26, 2017 (LTFU study, retrospectively registered).
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".