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.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".