Connected Care COPD: a community-led pathway for early detection and intervention
Bibliographic record
Abstract
Background: Chronic obstructive pulmonary disease (COPD) is a global health crisis. Up to 5% of adults live undiagnosed, leading to treatment delays and worsened health outcomes. The Connected Care COPD pathway, co-developed by the University Health Network and Toronto Paramedic Services, offers a community-based approach to early detection and care. Objective: To co-design, implement, and evaluate a care pathway, leveraging paramedic expertise, that improves early diagnosis, optimizes care delivery, and reduces healthcare utilization. Methods: A multidisciplinary team co-designed and launched the pathway in April 2024, with paramedic-led Wellness Clinics in high-priority settings such as Naturally Occurring Retirement Communities (NORCs) in Toronto, Canada. Clinics provided screening, spirometry, education, and tailored care plans. Results: The program launched at 3 NORCs with 1521 residents, 62% aged >65 years. Of the 117 residents (7.7%) participating in 4 clinics, 14 (12%) met criteria for further investigation of COPD based on a standardized screening questionnaire (mean age 72; 64% female). Mobile spirometry identified airway obstruction in 2 individuals, suggesting likely COPD. Care coordination led to 2 respirology referrals and 3 primary care referrals. Overall undiagnosed COPD prevalence was 1.7% (2/117) among total participants and 14% (2/14) among those screening positive. Conclusions: The Connected Care COPD program integrates paramedic-led services to bridge community and hospital care, providing a scalable model for COPD detection and management. Future plans include expanding to more high-risk populations and refining long-term evaluation metrics to improve patient outcomes.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".