Indications for Interferon/Ribavirin Therapy in Hepatitis C Patients: Findings from a Survey of Canadian Hepatologists
Bibliographic record
Abstract
OBJECTIVE: To survey practising hepatologists about their attitudes and practices regarding interferon and ribavirin combination therapy for hepatitis C (HCV) patients in Canada. METHODS: Anonymous fax and mail survey in Canada. The questionnaire consisted of two sets of questions: the likelihood (in percentage) of treating a patient with certain clinical characteristics; and opinions (Yes/No) regarding how his/her treatment decision is influenced by other factors (ie, patient age, genotype). Thirty-eight of 44 eligible participants responded to the survey with a response rate of 86.4%. RESULTS: Most participants indicated that they were likely to treat patients with "moderate/severe hepatitis with fibrosis" (median 80.0%), and compensated cirrhosis (median 75%). However, the participants were less willing to treat patients with coexisting conditions (median 25.0%) or mild hepatitis (median 13.8%). CONCLUSIONS: The findings from the present study indicate that there is a substantial variation in opinion among Canadian hepatologists towards treating HCV patients. The present study, however, suggests that the survey respondents appear, in general, to adhere to the HCV treatment guidelines by the Canadian Association for Study of the Liver.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".