A cross-sectional study of prolonged disengagement from clinic among people with HCV receiving care in a low-threshold, multidisciplinary clinic
Bibliographic record
Abstract
Background: Disengagement from care can affect treatment outcomes of patients with hepatitis C virus (HCV). We assessed the extent and determinants of disengagement among HCV patients receiving care at the Ottawa Hospital Viral Hepatitis Program (TOHVHP). Methods: We linked clinical data of adult patients, categorized as ever or never disengaged from clinic (no TOHVHP encounters over 18 months), receiving care between April 1, 2002, and October 1, 2015, to provincial health administrative databases and calculated primary care use in the year after disengagement. We used adjusted Cox proportional hazards models to analyze variables associated with disengagement. Results: Those disengaged from care ( n = 657) were younger at presentation (46.6 [SD 11.1] versus 51.9 [SD 11.0] years), p < 0.001) and had lower comorbidity. After multivariable adjustment, we observed lower hazards of disengagement among those with higher compared with lower fibrosis scores (F3, hazard ratio [HR] 0.21 [95% CI 0.08–0.57]; F4, HR 0.32 [95% CI 0.19–0.55]) and those treated compared with never treated (received direct-acting antivirals [DAAs], HR 0.71 [95% CI 0.58–0.88]; received interferon but not DAA, HR 0.66 [95% CI 0.55–0.80]). We found no association with mental health or substance use disorders. In the year after disengagement, 74.3% ( n = 488), 37.1% ( n = 244), and 17.7% ( n = 116) had at least one family physician visit, emergency department visit, and hospitalization, respectively. Conclusions: Better integration of HCV specialty and primary care could improve disengagement rates among people with HCV.
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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".