Dosing of <i>s</i>ublingual immunotherapy for allergic rhinitis: evidence‐based review with recommendations
Bibliographic record
Abstract
BACKGROUND: Since the mid 1980s, the clinical use of sublingual immunotherapy (SLIT) has dramatically increased. However, 1 of the primary barriers to providing SLIT is lack of a published dosing recommendations. The purpose of this work is to provide a range of effective SLIT dosing based upon a rigorous review of the existing evidence base. An appendix with SLIT dosing recommendations is also included. METHODS: A comprehensive search of the past 25 years of the medical literature using PubMed was performed for specific antigens. Inclusion criteria for articles included: randomized, placebo-controlled studies of SLIT, studies with clinical allergic rhinitis outcomes, and dosing units available to determine the micrograms per month of major allergen administered. The extracted data was used to compile a range of effective SLIT dosing for individual antigens. RESULTS: Seventy-five articles met the inclusion criteria, providing a range of effective dosing for some allergens. There was commonly a wide range in doses for particular antigens between the individual studies. For some antigens, there was significant overlap in dosage amount between studies showing efficacy and lack of efficacy. Clinical trials meeting inclusion criteria are not available for many allergens. CONCLUSION: This study provided a comprehensive review of the published sublingual dosing ranges for specific antigens. The review provided a range of effective sublingual doses for some allergens, whereas for other allergens there was insufficient published data to determine specific doses. Recommendations for SLIT dosing were produced based on the data revealed in the review and expert opinion.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".