Creating a provincial post <scp>COVID</scp>‐19 interdisciplinary clinical care network as a learning health system during the pandemic: Integrating clinical care and research
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
Introduction: Coronavirus Disease-2019 (COVID-19) affects multiple organ systems in the acute phase and also has long-term sequelae. Research on the long-term impacts of COVID-19 is limited. The Post COVID-19 Interdisciplinary Clinical Care Network (PC-ICCN), conceived in July 2020, is a provincially funded resource that is modelled as a Learning Health System (LHS), focused on those people with persistent symptoms post COVID-19 infection. Methods: The PC-ICCN emerged through collaboration among over 60 clinical specialists, researchers, patients, and health administrators. At the core of the network are the post COVID-19 Recovery Clinics (PCRCs), which provide direct patient care that includes standardized testing and education at regular follow-up intervals for a minimum of 12 months post enrolment. The PC-ICCN patient registry captures data on all COVID-19 patients with confirmed infection, by laboratory testing or epi-linkage, who have been referred to one of five post COVID-19 Recovery Clinics at the time of referral, with data stored in a fully encrypted Oracle-based provincial database. The PC-ICCN has centralized administrative and operational oversight, multi-stakeholder governance, purpose built data collection supported through clinical operations geographically dispersed across the province, and research operations including data analytics. Results: To date, 5364 patients have been referred, with an increasing number and capacity of these clinics, and 2354 people have had at least one clinic visit. Since inception, the PC-ICCN has received over 30 research proposal requests. This is aligned with the goal of creating infrastructure to support a wide variety of research to improve care and outcomes for patients experiencing long-term symptoms following COVID-19 infection. Conclusions: The PC-ICCN is a first-in-kind initiative in British Columbia to enhance knowledge and understanding of the sequelae of COVID-19 infection over time. This provincial initiative serves as a model for other national and international endeavors to enable care as research and research as care.
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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 | 0.038 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.011 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.012 |
| Insufficient payload (model declined to judge) | 0.000 | 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