Clinical validation of controlled grass pollen challenge in the Environmental Exposure Unit (EEU)
Bibliographic record
Abstract
RATIONALE: The Environmental Exposure Unit (EEU), a controlled allergen exposure model of allergic rhinitis (AR), has traditionally utilized ragweed pollen. We sought to clinically validate the use of grass pollen in the EEU. METHODS: Healthy volunteers with a history of AR symptoms during grass pollen season and supportive skin test responses attended the EEU for 3 hrs of rye grass pollen exposure (Lolium Perenne). Non-atopic controls were also recruited. Participants assessed individual rhinoconjunctivitis symptoms to generate Total Nasal Symptom Score (TNSS; max 12) and Total Symptom Score (TSS; max 24) and recorded Peak Nasal Inspiratory Flow (PNIF) q30min while in the EEU. Participants returned the following day for an additional 3 hrs of pollen exposure. Two separate groups allowed for the exploration of lower vs. higher pollen concentrations and subsequent effects on symptoms. RESULTS: 78 participants were screened, of whom 39 were eligible and attended the 2x3h EEU visits, plus 8 non-atopic controls. Mean TSS, TNSS and PNIF values amongst participants in the higher pollen concentration group (target 3500 grains/m3) after the first 3 hr exposure were 18.9, 9.7 and 68 L/min, respectively. In comparison, mean TSS, TNSS and PNIF values in the lower pollen concentration (2500 grains/m3) group were only 13.3, 7.6, and 82 L/min, respectively. The subsequent day of pollen exposure did not appreciably alter the maximal TSS/TNSSs, but rather resulted in a more rapid onset of symptomatology, with higher mean scores at the 30 min, 60 min and 90 min timepoints. The non-atopic controls remained asymptomatic. CONCLUSIONS: This study provides clinical validation of the ability to generate allergic rhinoconjunctivitis symptoms amongst grass-allergic individuals in the EEU.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".