Application of the health care system to First Nations and non-First Nations patients with chronic hepatitis C
Bibliographic record
Abstract
Approximat ely 240,000-300,000 persons are infected with hepatitis c virus (HCV) inCanada.However, there are no data on hepatitis C incidence, clinical features and management in canadian First Nations (FN).The present study examines the incidence and demographics of HCV in FN and non-First Nations (non-FN) persons and evaluates how HCV-infected Manitobans in these two subpopulations use the health care' Objectives: 1.To describe the incidence of hepatitis c (HC) by comparing rates and demographics of HCV infection in FN and non-FN populations' 2. To compare the clinical features between HCV-infected FN and non-FN individuals' 3. To compare health care ïesources utilization (1) between FN and non-FN individuals with hepatitis c and (2) between hepatitis c cohort and the general population' Methods: Multiple administrative and public health databases were linked to develop a comprehensive Hepatitis C Research Database.Between 111lT99I and 31'11212002,5018 HCV-positive Manitoba residents were identified.The demographically-matched population control cohort was drawn from the Population Registry' Demographic and clinical information, hospital separations, physician office visits, prescription drugs use' etc. were compared between FN and non-FN pefsons with HC as well as between HCV and non-HCV cohorts.Results: FN persons with HC were infected persons.While risk factors more often female and younger than non-FN HCVfor the progtession of HC to cirrhosis were doubled in the FN group, decompensated disease and mortality were the same in both groups.FN persons with HC had higher rates of health care use overall (hospital and ambulatory care), but lower rates of liver disease-related health care use compared to non-FN persons with HC.Finally, FN patients received antiviral treatment less often than non-FN patients.Conclusions.'The results of this study confirm that the rates of HC are higher among FN compared to non-FN persons yet liver disease-related care was less frequent among this group despite similarities in clinical features.Persons with HC used more health care compared to non-infected Manitobans.The created database facilitates designing subsequent projects to further examine HCV in Manitoba, to forecast the future burden of the disease, and to formulate specif,rc health progïammes of prevention and care.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".