The experience of pain among patients living with Hepatitis C: an assessment of prevalence and needs
Bibliographic record
Abstract
It is estimated that 300,000 individuals in Canada are infected with Hepatitis C (HCV). The pain experiences reported in relation to HCV appear to vary highly in both prevalence and source. Experiences of pain/pain treatment can be complicated by feelings of depression and poor sleep; whereby, pain can contribute to both depression and poorer sleep. This study will assess the prevalence and impact of pain among HCV patients from The Ottawa Hospital (TOH) including patient interest in various pain treatment options. A questionnaire study was conducted among HCV patients seen at the Viral Hepatitis Clinic at TOH between June-December 2008. The questionnaire package contained: Socio-demographics, CES-Depression Scale, Sleep Impairment Index, and Pain Treatment Preferences. 128 HCV patients met eligibility criteria for the study; 91 (71%) completed the survey. 56% of HCV patients reported chronic pain which commonly affected their back, legs, and joints. A majority (91%) reported that they would feel comfortable telling their healthcare providers about their pain. HCV patients with pain expressed a preference for visiting their family doctor and HCV specialist for pain treatment; almost half (47%) were interested in group-based pain management. Also, HCV patients with chronic pain reported significantly poorer sleep and greater feelings of depression.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".