Bibliographic record
Abstract
Abstract Asthma is one of the most common respiratory disorders in Canada, however, many Canadians with asthma remain poorly controlled. In most patients, control can be achieved through appropriate therapy, including: inhaled corticosteroids (ICS), combination ICS/long-acting beta 2 -agonists (LABA), “triple therapy” with ICS/LABA/long-acting muscarinic receptor antagonist (LAMA), and biologic therapies. The medical management of severe asthma, in particular, has changed dramatically with the incorporation of biologics in asthma treatment plans. Allergen-specific immunotherapy represents a potentially disease-modifying therapy for many patients with asthma; it must only be prescribed by physicians with appropriate training in allergy. Other essential components of asthma management include: regular monitoring of asthma control and risk of exacerbations; patient education and written asthma action plans; assessing barriers to treatment and adherence to therapy; adequate management of comorbidities (e.g., allergic rhinitis) and reviewing inhaler device technique. This article provides a review of current literature and guidelines for the appropriate diagnosis and management of asthma in adults and children.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.079 | 0.041 |
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".