Severe asthma assessment, management and the organisation of care in Australia and New Zealand: expert forum roundtable meetings
Bibliographic record
Abstract
Severe asthma imposes a significant burden on individuals, families and the healthcare system. Treatment is complex, due to disease heterogeneity, comorbidities and complexity in care pathways. New approaches and treatments improve health outcomes for people with severe asthma. However, emerging multidimensional and targeted treatment strategies require a reorganisation of asthma care. Consensus is required on how reorganisation should occur and what areas require further research. The Centre of Excellence in Severe Asthma convened three forums between 2015 and 2018, hosting experts from Australia, New Zealand and the UK. The forums were complemented by a survey of clinicians involved in the management of people with severe asthma. We sought to: (i) identify areas of consensus among experts; (ii) define activities and resources required for the implementation of findings into practice; and (iii) identify specific priority areas for future research. Discussions identified areas of unmet need including assessment and diagnosis of severe asthma, models of care and treatment pathways, add-on treatment approaches and patient perspectives. We recommend development of education and training activities, clinical resources and standards of care documents, increased stakeholder engagement and public awareness campaigns and improved access to infrastructure and funding. Further, we propose specific future research to inform clinical decision-making and develop novel therapies. A concerted effort is required from all stakeholders (including patients, healthcare professionals and organisations and government) to integrate new evidence-based practices into clinical care and to advance research to resolve questions relevant to improving outcomes for people with severe asthma.
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 | 0.089 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 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".