Extrapolating Evidence-Based Medicine of AIT Into Clinical Practice in the United States
Bibliographic record
Abstract
Allergy/immunology specialists in the United States prescribing allergy immunotherapy (AIT) have placed a heavy value on practical experience and anecdotal evidence rather than research-based evidence. With the extensive research on AIT conducted in the last few decades, the time has come to better implement evidence-based medicine (EBM) for AIT. The goal of this review was to critically assess EBM for debated concepts in US AIT practice for respiratory allergies in the context and quality of today's regulatory standards. Debated topics reviewed were the efficacy and safety of AIT in various subgroups (eg, polyallergic patients, older patients, patients with asthma, and pregnant women), diagnosis topics (eg, skin prick test vs allergen-specific serum IgE, factors affecting skin prick tests, use of nasal or conjunctival allergen challenges, and telemedicine for diagnosis), and dosing topics (eg, optimal dosing for subcutaneous immunotherapy and sublingual immunotherapy tablets, US liquid allergen extract history, duration of treatment, and biomarkers of efficacy). In addition, EBM for patient-centered AIT issues (eg, adherence, use of practice guidelines, and pharmacoeconomics) and the approach to implementation of AIT EBM in future clinical practice were addressed. The EBM for each concept was briefly summarized, and when possible, a practical, concise recommendation was given.
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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".