Major Care Gaps in Asthma, Sleep and Chronic Obstructive Pulmonary Disease: A Road Map for Knowledge Translation
Bibliographic record
Abstract
Large gaps between best evidence-based care and actual clinical practice exist in respiratory medicine, and carry a significant health burden. The authors reviewed two key care gaps in each of asthma, chronic obstructive pulmonary disease and obstructive sleep apnea. Using the 'Knowledge-to-Action Framework', the nature of each gap, its magnitude, the barriers that cause and perpetuate it, and past and future strategies that might address the problem were considered. In asthma: disease control is ascertained inadequately, leading to a prevalence of poor asthma control of approximately 50%; and asthma action plans, a key component of asthma management, are provided by only 22% of physicians. In obstructive sleep apnea: disease is under-recognized, with sleep histories ascertained in only 10% of patients; and Canadian polysomnography wait times remain longer than recommended, leading to unnecessary morbidity and societal cost. In chronic obstructive pulmonary disease: a large proportion of patients seen in primary care remain undiagnosed, mainly due to underuse of spirometry; and <10% of patients are referred for pulmonary rehabilitation, despite strong evidence demonstrating its cost effectiveness. Given the prevalence of these chronic conditions and the size and nature of these gaps, the latter exact an important toll on patients, the health care system and society. In turn, complex barriers at the patient, provider and health care system levels contribute to each gap. There have been few previous attempts to bridge these gaps. Innovative and multifaceted implementation approaches are needed and have the potential to make a large impact on Canadian respiratory health.
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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 | 0.122 | 0.191 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.026 | 0.040 |
| Open science | 0.009 | 0.024 |
| Research integrity | 0.022 | 0.035 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".