Loss to follow‐up: A significant barrier in the treatment cascade with direct‐acting therapies
Bibliographic record
Abstract
Effectiveness of direct-acting antiviral (DAA) therapies could be influenced by patient characteristics such as comorbid conditions, which could lead to premature treatment discontinuation and/or irregular medical follow-ups. Here, we evaluate loss to follow-up and treatment effectiveness of sofosbuvir/ledipasvir ± ribavirin (SOF/LDV ± RBV), ombitasvir/paritaprevir/ritonavir + dasabuvir ± ribavirin (OBV/PTV/r + DSV ± RBV) for hepatitis C virus (HCV) genotype 1 (GT1) and sofosbuvir + ribavirin (SOF + RBV) for genotype 3 (GT3) in British Columbia Canada: The British Columbia Hepatitis Testers Cohort includes data on individuals tested for HCV since 1992, integrated with medical visit, hospitalization and prescription drug data. HCV-positive individuals who initiated DAA regimens, irrespective of treatment completion, for GT1 and GT3 until 31 December, 2017 were included. Factors associated with sustained virological response (SVR) and loss to follow-up were assessed by using multivariable logistic regression models. In total 4477 individuals initiated DAAs. The most common prescribed DAA was SOF/LDV ± RBV with SVR of 95%. The highest SVR of 99.5% was observed among OBV/PTV/r + DSV-treated patients. Overall, 453 (10.1%) individuals were lost to follow-up. Higher loss to follow-up was observed among GT1 patients treated with OBV (17.8%) and GT3 patients (15.7%). The loss to follow-up rate was significantly higher among individuals aged <60 years, those with a history of injection drug use (IDU), on opioid substitution therapy and with cirrhosis. Our findings indicate that loss to follow-up exceeds viral failure in HCV DAA therapy and its rate varies significantly by genotype and treatment regimen. Depending on the aetiology of lost to follow-up, personalized case management for those with medical complications and supporting services among IDU are needed to achieve the full benefits of effective treatments.
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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 | 0.026 | 0.136 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".