Bibliographic record
Abstract
Sublingual immunotherapy (SLIT)-tablets represent a new allergen immunotherapy option for clinicians. In North America, there are five SLIT-tablets approved for the treatment of allergic rhinoconjunctivitis (ARC). No SLIT-drops products are currently approved in the United States or Canada. This work reviewed the efficacy of the timothy grass SLIT-tablet, five-grass SLIT-tablet, ragweed SLIT-tablet, house-dust mite SLIT-tablet, and tree SLIT-tablet in patients with ARC. All the SLIT-tablets showed consistent clinical efficacy for the treatment of ARC in large, double-blind, placebo-controlled trials, including for both patients who were monosensitized and those who were polysensitized. Treatment with house-dust mite SLIT-tablet has shown efficacy in patients who are pollen sensitized during their respective pollen seasons. In contrast to SLIT-tablets, efficacy studies of SLIT-drops show high heterogeneity of treatment effect. Although data are scarce, data that compared the efficacy of SLIT-tablets versus ARC pharmacotherapy generally indicated that SLIT-tablets had a greater benefit than pharmacotherapy when compared with placebo, particularly for perennial ARC. When compared with subcutaneous immunotherapy, analysis of these data indicated that SLIT-tablets had a benefit over subcutaneous immunotherapy in regard to safety but somewhat less benefit in regard to efficacy. The safety of SLIT-tablets has been well documented, and a U.S. Food and Drug Administration class label with safety considerations is present in the prescribing information for all SLIT-tablets. No new safety signals have been observed after reinitiating SLIT-tablets after a short treatment interruption.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".