Adult Severe Asthma Registries: A Global and Growing Inventory
Bibliographic record
Abstract
Aim: The International Severe Asthma Registry (ISAR; http://isaregistries.org/) uses standardised variables to enable multi-country and adequately powered research in severe asthma. This study aims to look at the data countries within ISAR and non-ISAR countries reported collecting that enable global research that support individual country interests. Methods: Registries were identified by online searches and approaching severe asthma experts. Participating registries provided data collection specifications or confirmed variables collected. Core variables (results from ISAR's Delphi study), steroid-related comorbidity variables, biologic safety variables (serious infection, anaphylaxis, and cancer), COVID-19 variables and additional variables (not belonging to the aforementioned categories) that registries reported collecting were summarised. Results: Of the 37 registries identified, 26 were ISAR affiliates and 11 non-ISAR affiliates. Twenty-five ISAR-registries and 4 non-ISAR registries reported collecting >90% of the 65 core variables. Twenty-three registries reported collecting all optional steroid-related comorbidity variables. Twenty-nine registries reported collecting all optional safety variables. Ten registries reported collecting COVID-19 variables. Twenty-four registries reported collecting additional variables including data from asthma questionnaires (10 Asthma Control Questionnaire, 20 Asthma Control Test, 11 Asthma Quality of Life Questionnaire, and 4 EuroQol 5-dimension 5-level Questionnaire). Eight registries are linked to databases such as electronic medical records and national claims or disease databases. Conclusion: Standardised data collection has enabled individual severe asthma registries to collect unified data and increase statistical power for severe asthma research irrespective of ISAR affiliations.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".