Evaluation of immigration status, race and language barriers on chronic hepatitis C virus infection management and treatment outcomes
Bibliographic record
Abstract
OBJECTIVE: Hepatitis C virus (HCV) prevalence in certain Canadian immigrant populations is higher than that of the overall population. Disparities in care related to immigration status as well as to race and language are well recognized. Identifying and understanding these disparities is vital to the provision of optimal and inclusive HCV care. METHODS AND MATERIALS: HCV RNA-positive patients assessed at The Ottawa Hospital Viral Hepatitis Clinic between June 2000 and June 2007 were identified using a clinical database. As measures of access to care, liver biopsy rates, treatment initiation rates, supportive care provision (i.e. erythropoietin for treatment-related anemia) and sustained virological response (SVR) rates were assessed as a function of immigration status, race and spoken language. RESULTS: Nine hundred and ten patients were evaluated, of which 20% were immigrants. Biopsy rates (54 vs. 51%), HCV treatment initiation (37 vs. 38%), erythropoietin prescription (13 vs. 18%) and SVR rates (52 vs. 51%) did not differ between immigrants and Canadian-born individuals. Spoken language and race did not influence access to treatment. SVR was predicted by genotype, HIV status and race. CONCLUSION: In the context of a multidisciplinary, multilingual universal health care system, by studying the influence of barriers to HCV investigation and successful therapy can be abrogated.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".