Developments in the field of allergy in 2011 through the eyes of <i>Clinical and Experimental Allergy</i>
Bibliographic record
Abstract
As in previous years, we felt it would be of value to our readership to summarize the new information provided by the authors who have published in Clinical and Experimental Allergy in 2011 and set this in the context of recent advances in our understanding of the pathogenesis and management of allergic disease in all its many manifestations. In 2011, about 210 articles were published in Clinical and Experimental Allergy including editorials, reviews, opinion articles, guidelines, letters, book reviews and of course at the heart of the journal, papers containing original data. As before, this review is divided into sections based on the way the journal is structured, although this year we have grouped together all the papers dealing with mechanisms of allergic disease, whether they involve patients (clinical mechanisms), pure in vitro studies (basic mechanisms) or animal models (experimental models), as we felt this was a more coherent way to deal with the subject. In the field of asthma and rhinitis, the relationship between airway inflammation and airway dysfunction was of perennial interest to investigators, as were phenotypes and biomarkers. Aspirin hypersensitivity appeared in studies in several papers and there was new interest in asthma in the elderly. The mechanisms involved in allergic disease describe advances in our understanding of T cell responses, the relationship between inflammation and disease, mast cell and basophil activation, steroid resistance and novel therapies. In the section dealing with epidemiology, studies seeking to identify risk factors for allergic disease including vitamin D are prominent, as once again are studies investigating gene-environment interactions. The clinical allergy section focuses on drug allergy, food allergy and immunotherapy. The area of oral immunotherapy for food allergy is well covered and we were grateful to Stephen Durham for guest editing an outstanding special issue on immunotherapy in the centenary year of Leonard Noon's pioneering work. Lastly, in the field of allergens, the interest in component-resolved diagnosis continues to grow and there are also articles describing important novel cultivars and the effect of food processing on the allergenic properties of foods. Another terrific year, full of important and high-quality work,which the journal has been proud to bring to the allergy community.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".