Impact of an Accelerated Pretreatment Evaluation on Linkage-to-Care for Hepatitis C-infected Persons Who Inject Drugs
Bibliographic record
Abstract
Background: Historically, hepatitis C virus (HCV) pretreatment evaluation has required multiple visits, frequently resulting in loss to follow-up and a delayed initiation of treatment. New technologies can accelerate this process. We investigated the feasibility of a single-day evaluation program and its impact on evaluation completion, treatment eligibility awareness, and treatment initiation among people who inject drugs (PWIDs). Methods: HCV-infected PWID who were unaware if they were eligible for treatment were recruited in a prospective evaluation of an accelerated model of care between 2017 and 2019 and compared to a historical cohort. The patients underwent a medical evaluation, rapid HCV viral load testing, and transient elastography during a single visit, at the end of which they were informed whether they were eligible for treatment. A historical cohort of patients fulfilling the same inclusion criteria and evaluated with the usual standard of care spanning several visits who were examined at the addiction medicine clinic from 2014 to 2016 served as the comparison group. Results: The accelerated and historical cohorts included 99 and 76 patients, respectively. The cohorts did not differ significantly by age and gender, but more patients in the historical cohort were undergoing opioid agonist therapy, while more patients in the accelerated cohort injected drugs in the last month. An accelerated evaluation resulted in a higher rate of evaluation completion (100% vs 67.1%; P < .001). Among those eligible for treatment, the proportion of those initiating treatment was similar between the groups (51/64 (79.7%) vs. 26/37 (70.3%); P = .28). The delay in the initiation of treatment was shorter in the accelerated cohort than in the historical cohort (69 (IQR: 49-106) days vs. 219 (IQR: 141-416) days; P < .001). Conclusions: Accelerated evaluation enhanced the awareness of eligibility and reduced the time to initiation among eligible patients. Trial Registration: This study is registered on www.clinicaltrials.gov (NCT02755402).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".