Beliefs and preferences regarding biological treatments for severe asthma
Bibliographic record
Abstract
BACKGROUND: Severe asthma is a serious condition with a significant burden on patients' morbidity, mortality, and quality of life. Some biological therapies targeting the IgE and interleukin-5 (IL5) mediated pathways are now available. Due to the lack of direct comparison studies, the choice of which medication to use varies. We aimed to explore the beliefs and practices in the use of biological therapies in severe asthma, hypothesizing that differences will occur depending on the prescribers' specialty and experience. METHODS: We conducted an online survey composed of 35 questions in English. The survey was circulated via the INterasma Scientific Network (INESNET) platform as well as through social media. Responses from allergists and pulmonologists, both those with experience of prescribing omalizumab with (OMA/IL5) and without (OMA) experience with anti-IL5 drugs, were compared. RESULTS: Two hundred eighty-five (285) valid questionnaires from 37 countries were analyzed. Seventy-on percent (71%) of respondents prescribed biologics instead of oral glucocorticoids and believed that their side effects are inferior to those of Prednisone 5 mg daily. Agreement with ATS/ERS guidelines for identifying severe asthma patients was less than 50%. Specifically, significant differences were found comparing responses between allergists and pulmonologists (Chi-square test, p < 0.05) and between OMA/IL5 and OMA groups (p < 0.05). CONCLUSIONS: Uncertainties and inconsistencies regarding the use of biological medications have been shown. The accuracy of prescribers to correctly identify asthma severity, according to guidelines criteria, is quite poor. Although a substantial majority of prescribers believe that biological drugs are safer than low dose long-term treatment with oral steroids, and that they must be used instead of oral steroids, every effort should be made to further increase awareness. Efficacy as disease modifiers, biomarkers for selecting responsive patients, timing for outcomes evaluation, and checks need to be addressed by further research. Practices and beliefs regarding the use of asthma biologics differ between the prescriber's specialty and experience; however, the latter seems more significant in determining beliefs and behavior. Tailored educational measures are needed to ensure research results are better integrated in daily practice.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".