Depressive symptoms are no longer a barrier to HCV treatment initiation in the HIV–HCV co-infected population in Canada
Bibliographic record
Abstract
Background Psychiatric illness was a major barrier for HCV treatment during the Interferon (IFN) treatment era due to neuropsychiatric side effects. While direct acting antivirals (DAA) are better tolerated, patient-level barriers persist. We aimed to assess the effect of depressive symptoms on time to HCV treatment initiation among HIV–HCV co-infected persons during the IFN (2003–2011) and second-generation DAA (2013–2020) eras. Methods We used data from the Canadian Co-infection Cohort, a multicentre prospective cohort, and its associated sub-study on Food Security (FS). We predicted Center for Epidemiologic Studies Depression Scale-10 (CES-D-10) classes for depressive symptoms indicative of a depression risk using a random forest classifier and corrected for misclassification using predictive value-based record-level correction. We used marginal structural Cox proportional hazards models with inverse weighting for competing risks (death) to assess the effect of depressive symptoms on treatment initiation among HCV RNA-positive participants. Results We included 590 and 1127 participants in the IFN and DAA eras. The treatment initiation rate increased from 9 (95% confidence interval (CI): 7–10) to 21 (95% CI: 19–22) per 100 person-years from the IFN to DAA era. Treatment initiation was lower among those with depressive symptoms compared to those without in the IFN era (hazard ratio: 0.81 (95% CI: 0.69–0.95)) and was higher in the DAA era (1.19 (95% CI: 1.10–1.27)). Conclusion Depressive symptoms no longer appear to be a barrier to HCV treatment initiation in the co-infected population in the DAA era. The higher rate of treatment initiation in individuals with depressive symptoms suggests those previously unable to tolerate IFN are now accessing treatment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".