Improving primary care management of asthma: do we know what really works?
Bibliographic record
Abstract
Asthma imposes a substantial burden on individuals and societies. Patients with asthma need high-quality primary care management; however, evidence suggests the quality of this care can be highly variable. Here we identify and report factors contributing to high-quality management. Twelve primary care global asthma experts, representing nine countries, identified key factors. A literature review (past 10 years) was performed to validate or refute the expert viewpoint. Key driving factors identified were: policy, clinical guidelines, rewards for performance, practice organisation and workforce. Further analysis established the relevant factor components. Review evidence supported the validity of each driver; however, impact on patient outcomes was uncertain. Single interventions (e.g. healthcare practitioner education) showed little effect; interventions driven by national policy (e.g. incentive schemes and teamworking) were more effective. The panel's opinion, supported by literature review, concluded that multiple primary care interventions offer greater benefit than any single intervention in asthma management.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| 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".