Analysis of allergen immunotherapy studies shows increased clinical efficacy in highly symptomatic patients
Bibliographic record
Abstract
BACKGROUND: The assessment of allergen immunotherapy (AIT) efficacy in the treatment for seasonal allergic rhinoconjunctivitis (SAR) symptoms is challenging. Allergen immunotherapy differs from symptomatic therapy in that while symptomatic therapy treats patients after symptoms appear and aims to reduce symptoms, AIT is administered before symptoms are present and aims to prevent them. Thus, clinical studies of AIT can neither establish baseline symptom levels nor limit the enrolment of patients to those with the most severe symptoms. Allergen immunotherapy treatment effects are therefore diluted by patients with low symptoms for a particular pollen season. The objective of this analysis was to assess the effect possible to achieve with AIT in the groups of patients presenting the most severe allergic symptoms. METHODS: Study centres were grouped into tertiles categorized according to symptom severity scores observed in the placebo patients in each centre (low, middle and high tertiles). The difference observed in the average score in each tertile in active vs placebo-treated patients was assessed. This allowed an estimation of the efficacy that could be achieved in patients from sites where symptoms were high during the pollen season. RESULTS: An increased treatment effect was observed in the most severe patients and was independent of the study analysed and symptom score used. CONCLUSIONS: The use of a tertile approach to analyse efficacy in AIT in SAR clinical studies can give a more accurate assessment of potential clinical benefit.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".