Reducing the hidden burden of severe asthma: recognition and referrals from primary practice
Bibliographic record
Abstract
Since their introduction many decades ago, systemic corticosteroids have become a mainstay treatment for asthma. Despite being a highly effective therapy, corticosteroids can cause significant adverse effects in patients. This results in a “double hit” for some patients as they suffer the burden of disease as well as the burden of treatment-induced morbidity.This article aims to raise awareness of the potential, harmful side effects of prolonged or repeated exposure to systemic corticosteroids in asthma. It also highlights the importance of referral of the appropriate patients with asthma from primary care for specialist assessment once other considerations such as adherence, inhaler technique and co-morbidity have been evaluated. We propose a simple decision step that may help busy primary care physicians and general practitioners to identify patients who could benefit from specialist assessment.Our decision step suggests that a patient with asthma should be reviewed at least once by an asthma specialist if he/she (i) has received ≥2 courses of oral corticosteroids in the previous year; asthma remains uncontrolled despite good adherence and inhaler technique; or (ii) has attended an emergency department or was hospitalized for asthma care.Such referral could facilitate wider access to diagnostic tools, in-depth assessment of confounding comorbidities, and non-corticosteroid-based therapies as needed, which may be unavailable in primary practice.
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 | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".