Prioritising respiratory research needs in primary care: results from the International Primary Care Respiratory Group (IPCRG) global e-Delphi exercise
Bibliographic record
Abstract
Respiratory diseases impose a significant burden on global morbidity and mortality accounting for 7.7m deaths/yr. Primary care plays an essential role in the prevention, diagnosis and management of respiratory diseases, and relevant evidence-based guidelines are required. However, there is a lack of investment in primary care respiratory research and an up-to-date prioritised research needs statement should help to bridge this gap. An e-Delphi exercise was conducted to identify and prioritise the most important respiratory research questions and topics relevant to primary care clinicians globally. Participants included 112 community-based physicians, nurses and other healthcare professionals from 27 high-, middle-, and low-income countries. 608 initial research questions were suggested by participants, then refined to 176 questions through review by academic experts. Questions included topics relevant to the diagnosis, management, monitoring, self-management and prognosis of asthma, COPD and other respiratory conditions. Following 2 rounds of rating, 49 questions reached 80% consensus, which was based on importance and clinical relevance. The top 5 ranked questions concerning the best ways in primary care to manage chronic cough; monitor asthma; prevent exacerbations and progression of asthma; deliver brief advice to quit tobacco use; manage COPD patients with cardiovascular comorbidities.
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 | 0.141 | 0.187 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".