The Evolution of a Shared Care Model for Chronic Hepatitis B
Bibliographic record
Abstract
Greater than 70% of new arrivals to Canada come from Hepatitis B endemic areas. Due to a lack of screening and access to follow up, these patients subsequently face an 8.4-fold higher mortality rate from this disease than the Canadian population. Additionally, the provision of healthcare to newly arrived refugees is challenging, given the complexity of their physical and psychological health needs and resettlement challenges. In 2011 the Mosaic Refugee Health Clinic began a shared-care model for monitoring Hepatitis B patients. In 2016, the system evolved to include the creation of an EMR care flowsheet and the incorporation of non-clinical team members. Since September 2016 close to 200 cases have been followed. Family physicians identify and monitor chronically infected patients, reviewing cases with Hepatology based on complexity and acuteness. A database and processes for monitoring patients were developed within the EMR. The system includes automated recalls and staff who track and ensure patients attend appointments, emphasizing patient education. Evaluation and assessment of this initiative is ongoing and will review prevalence rates of hepatitis B infection in adult seen at the MRHC between 2011-2018. Additionally, rates of complications and adherence to routine surveillance will be investigated. Current adherence suggests that 98% of active patients have had the appropriate initial work up and monitoring. This unique model serves as an example of improving care through integrating specialty support and empowering a primary health care multidisciplinary team. Physician collaboration, staff involvement and EMR utilization have likely been keys to success.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".