International Severe Asthma Registry
Bibliographic record
Abstract
Regional and/or national severe asthma registries provide valuable country-specific information. However, they are often limited in scope within the broader definitions of severe asthma, have insufficient statistical power to answer many research questions, lack intraoperability to share lessons learned, and have fundamental differences in data collected, making cross comparisons difficult. What is missing is a worldwide registry which brings all severe asthma data together in a cohesive way, under a single umbrella, based on standardized data collection protocols, permitting data to be shared seamlessly. The International Severe Asthma Registry (ISAR; http://isaregistries.org/) is the first global adult severe asthma registry. It is a joint initiative where national registries (both newly created and preexisting) retain ownership of their own data but open their borders and share data with ISAR for ethically approved research purposes. Its strength comes from collection of patient-level, anonymous, longitudinal, real-life, standardized, high-quality data (using a core set of variables) from countries across the world, combined with organizational structure, database experience, inclusivity/openness, and clinical, academic, and database expertise. This gives ISAR sufficient statistical power to answer important research questions, sufficient data standardization to compare across countries and regions, and the structure and expertise necessary to ensure its continuance and the scientific integrity and clinical applicability of its research. ISAR offers a unique opportunity to implement existing knowledge, generate new knowledge, and identify the unknown, therefore promoting new research. The aim of this commentary is to fully describe how ISAR may improve our understanding of severe asthma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.012 |
| 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) | 0.018 | 0.005 |
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".