National and regional asthma programmes in Europe
Bibliographic record
Abstract
This review presents seven national asthma programmes to support the European Asthma Research and Innovation Partnership in developing strategies to reduce asthma mortality and morbidity across Europe. From published data it appears that in order to influence asthma care, national/regional asthma programmes are more effective than conventional treatment guidelines. An asthma programme should start with the universal commitments of stakeholders at all levels and the programme has to be endorsed by political and governmental bodies. When the national problems have been identified, the goals of the programme have to be clearly defined with measures to evaluate progress. An action plan has to be developed, including defined re-allocation of patients and existing resources, if necessary, between primary care and specialised healthcare units or hospital centres. Patients should be involved in guided self-management education and structured follow-up in relation to disease severity. The three evaluated programmes show that, thanks to rigorous efforts, it is possible to improve patients' quality of life and reduce hospitalisation, asthma mortality, sick leave and disability pensions. The direct and indirect costs, both for the individual patient and for society, can be significantly reduced. The results can form the basis for development of further programme activities in Europe.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".