Major Care Gaps in Asthma, Sleep and Chronic Obstructive Pulmonary Disease: A Road Map for Knowledge Translation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it