741Management of Hepatitis C Among Infectious Diseases Physicians: Current Practice and Opinions
Bibliographic record
Abstract
Background. Hepatitis C (HCV) is a prevalent cause of morbidity and mortality. Updated screening guidelines, public awareness campaigns, and new direct-acting antiviral agents are likely to increase the number of patients seeking HCV care. Infectious diseases (ID) physicians have been identified as a group well suited to manage HCV, but the current and anticipated role of ID physicians has not been sufficiently evaluated. Methods. Adult ID physicians were surveyed regarding their opinions and current practices related to HCV care through the Emerging Infections Network (EIN) via a 10-question survey. Results. Of 1,172 EIN members in the U.S., Canada, and Puerto Rico, 550 (47%) responded. Most (71%) responded that ID physicians should evaluate and/or treat all HCV infections with gastroenterology/hepatology support, while a minority (25%) responded that ID physicians should only evaluate and/or treat patients with mild-moderate liver fibrosis or HIV co-infection. Overall, 54% of respondents currently evaluate and/or treat HCV infection in some capacity, either as HCV mono-infection (40%) and/or HIV/HCV co-infection (47%). Fifty-two percent of physicians who do not currently evaluate and/or treat HCV mono-infection indicated interest in doing so in the future. Factors influencing this decision include clinical capacity/infrastructure, interferon-free regimens for all genotypes, and training/experience. Respondents who do not plan to evaluate and/or treat HCV mono-infection in the future (27%) most commonly cited insufficient capacity/infrastructure, lack of desire, and inadequate training/experience as their rationale. Most ID physicians (61%) did not feel that graduate medical education prepared them to evaluate and/or treat HCV, and members indicated a need for a broad range of training resources. Conclusion. More than 90% of respondents believe that ID physicians should be active in HCV management. The majority of ID physicians who wish to manage HCV mono-infection already provide this service, although many may increase this area of their practice. Expanding graduate medical education, emphasizing continuing medical education, and developing novel management paradigms will be necessary to optimize HCV care in the future. Disclosures. All authors: No reported disclosures.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".