Bibliographic record
Abstract
Despite recent developments in our understanding of what could be the optimal management of asthma and renewed efforts from guidelines developers to help clinicians integrate their recommendations into current care, asthma remains a major human and economic burden [1–3]. Asthma is a variable condition, which unfortunately often remains uncontrolled, resulting in frequent acute healthcare use and impaired quality of life. Patient involvement in the management of their condition helps improve its control, and most patients agree to play such a role [4, 5]. However, to be able to manage asthma adequately, those suffering from asthma should understand the nature of the disease, how to assess its control, the basic principles of treatment and the peculiarities associated with their own case, in addition to learning essential self-management skills [4, 6, 7]. Self-management asthma education is therefore considered by current asthma guidelines and strategies to be an essential component in the management of asthma, and is recommended with the highest level of evidence [8–10]. Targeted simple educational interventions including key messages to the patient can help reduce asthma burden
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.023 | 0.026 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".