Bibliographic record
Abstract
Background: We undertook a randomised, double-blind, double-dummy, controlled trial of subcutaneous and sublingual grass pollen immunotherapy.Method: Nasal allergen challenge with grass pollen extract (Aquagen SQ, Phleum pratense, ALK, Denmark) was carried out in 106 participants before randomisation to one of three arms: active-SCIT (Alutard SQ, ALK), active-SLIT (Grazax, ALK) or double-placebo.Repeat challenges were carried out at 1 and 2 years of treatment, then again after 12 months off treatment (year 3).Response to challenge, acutely and over 10 h, was assessed by total nasal symptom score (TNSS, scale: 0-12) and peak nasal inspiratory flow (PNIF, l/min).Nasal fluid was collected and analysed by immunoassay.Results: SCIT inhibited TNSS at 1 year compared to placebo (P < 0.01) and SLIT (P = 0.03).SLIT did not reduce TNSS at 1 year (P = 0.13 vs placebo); both treatments reduced TNSS at 2 years (SCIT P < 0.01; SLIT P = 0.02), with no difference between the two (P = 0.20).Both treatments improved PNIF at years 1 and 2 vs placebo (SCIT P < 0.01, P < 0.01; SLIT P = 0.02, P = 0.01).Nasal fluid interleukin 2 (IL-2), IL-4, IL-5, and IL-13 were equally reduced by both treatments (all P < 0.05); there was no effect on IFN-c, IL-10, IL-12p70, tryptase or ECP.Clinical and immunological effects were not maintained at year 3. Conclusion: Two years SCIT or SLIT is effective in suppressing allergen-induced nasal responses and local Th2 cytokines; this effect is not apparent at year 3 which suggests that two years' treatment is insufficient for long-term tolerance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.844 | 0.648 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".